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2019 Fall Meeting



Novel Approaches for Neuromorphic Computing: Materials, Concepts and Devices

Energy consumption and data storage limit the development of modern electronics. This motivates the search for alternative ways of information processing. Bioinspired electronics based-on non-volatile memory devices is most promising. Various materials, technologies and computational schemes can be used for implementing artificial synapses and neurons in so-called neuromorphic networks.


Neuromorphic engineering exploiting non-volatile memory (NVM) devices, or in general memristive systems), has enormous potential for highly energy-efficient cognitive electronics being clearly superior to state-of-the-art computing architectures in terms of data- and energy consumption. To mimic the role of synapses in the nervous systems and the stochastic and non-linear characteristics of neuronal units, a large number of materials and device structures are investigated for neuromorphic systems. Furthermore, a variety of different computational approaches is already presented taking into account the special properties of those devices and various architectures, also exploiting hybrid CMOS-NVM systems. For example, the binary On/Off switching behavior, as explored in Conductive-Bridging RAM (CBRAM) or Magnetic RAM devices can be used to emulate synaptic functionality. Phase Change Memory (PCM) or Resistive-RAM (RRAM), are as well highly employed to reconstruct phenomenological substitutes of synapses for neural networks. Furthermore, the inherent stochasticity and multilevel, or analog, capability of some NVM devices are used to emulate synaptic learning and memory processes. However, in order to realize powerful neuromorphic electronics, an interdisciplinary approach is needed that ranges from the investigation of material properties, through the realization of functional devices, to novel computing and circuit schemes. In order to maintain this interdisciplinary, an intensive exchange of scientists with very different expertise is required. This includes experts in the field of novel materials, analytics and measurement methods, as well as device experts, developers of computational and biological modeling, circuit designers and experts in the field of complex dynamic systems. Therefore, the symposium seeks to provide the framework for an interdisciplinary exchange of scientists from those various fields.

Hot topics to be covered by the symposium:

  • Physics and technology of memristive nanomaterials and devices
  • Stochastic phenomena in memristive materials
  • Engineering of different technologies devices (RRAM, CBRAM, MRAM, FeRAM, PCM) for spiking neural networks
  • Devices to emulate the synaptic or neuron functionality
  • Emerging materials and concepts for neuromorphic computing: 2D materials, organic synaptic devices, optical switching
  • Design and modelling of neuromorphic computing systems
  • Bio-hybrid network
  • Organic Electronics for Neuromorphic Computing
  • Photonic neural networks

Scientific committee:

  • Ming Liu (IMECAS, China)
  • Stefano Brivio (CNR-IMM, Italy)
  • Gang Niu (Xi’an Jiatong University, China)
  • Hangbing Lv (CAS, China)
  • Jinfeng Kang (Peking University, China)
  • Doo Seok Jeong (Hanyang University, Seoul, South Korea)
  • Enrique Alberto Miranda (UAB, Spain)
  • Christophe Vallee (LTM, France)
  • Julie Grollier (CNRS-Thales, France)
  • Lambert Alff (TU Darmstadt, Germany)
  • Gennadi Bersuker (Aerospace Corporation, USA)
  • Regina Dittmann (FZ Juelich, Germany)
  • Elisabetta Chicca (Uni Bielefeld, Germany)

Invited speakers:

  • Huaqiang Wu (Tsinghua University Beijin, China)
  • Daniele Ielmini (Politecnio of Milano, Italy)
  • Daniel Brunner (Institut FEMTO-ST, Besançon, France)
  • Viktor Erokhin (University of Parma, Italy)
  • Stefan Slesazeck (Namlab Dresden, Germany)
  • Herbert Jaeger (Jakobs University Bremen, Germany)
  • Hyunsang Hwang (POSTECH, Korea)
  • Damien Querlioz (Centre de Nanosciences et de Nanotechnologies, France)


Selected papers will be published in Physica Status Solidi (a) - Wiley.

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Materials Concepts -RRAM : Christian Wenger
Authors : Huaqiang Wu, Bin Gao, Jianshi Tang, and He Qian
Affiliations : Institute of Microelectronics, Tsinghua University, Beijing, China

Resume : RRAM device with linear analog switching behavior and excellent uniformity is particularly important for neuromorphic computing. However, nonlinear abrupt switching and large device to device variations are often observed in filamentary RRAM devices. In this work, the impacts of electric field, temperature and formation energy of oxygen vacancy (Vo) on analog switching behaviors and device uniformity are discussed in detail. Then an electro-thermal modulation method and is proposed and demonstrated to optimize the analog switching behavior. And a localizing Vo formation method is proposed and demonstrated to optimize device uniformity. By optimizing the thermal conductivity and resistivity of the capping layer and uniform doping to lower Vo formation energy in the resistive switching layer, a fab-friendly HfO2 based analog RRAM device is developed. Based on the proposed method, excellent linear analog behaviors and good uniformity are realized on a RRAM array. The proposed methodologies provide a valuable guideline for designing high performance and large-scale RRAM based neuromorphic computing chips.

Authors : S. Hoffmann-Eifert*, F. Cüppers*, S. Menzel*, C. Bengel†, A. Hardtdegen*, M. von Witzleben†, U. Böttger†, R. Waser*†
Affiliations : * Peter Grünberg Institute (PGI 7&10) and JARA-Fit, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany † Institute of Materials in Electrical Engineering and Information Technology II and JARA-Fit, RWTH Aachen University, 52062 Aachen, Germany

Resume : The utilisation of bipolar-type memristive devices for the realisation of synaptic connectivity in neural networks strongly depends on the ability of the devices for analogue conductance modulation under application of electrical stimuli in the form of identical voltage pulses. Typically, filamentary valence change mechanism (VCM)-type devices show an abrupt SET and a gradual RESET switching behaviour. Thus, an analogue conductance modulation during SET and RESET is difficult to attain. Here, we show that analogue as well as binary conductance modulation can be achieved in a Pt/HfO2/TiOx/Ti VCM cell by proper adjustment of the operation conditions. By analysing the switching dynamics over many orders of magnitude and comparison to a fully dynamic switching model, the origin of the two different switching modes is revealed. SET and RESET transition show a two-step switching process: a fast conductance change succeeds a slow conductance change. While the time for the fast conductance change, the transition time, turns out to be state-independent for a specific voltage, the time for the slow conductance change, the delay time, is highly state-dependent. Analogue switching can be achieved if the pulse time is a fraction of the transition time. If the pulse time is larger than the transition time, the switching becomes probabilistic and binary. Considering the effect of the device state on the delay time in addition, a procedure is proposed to find the ideal operation conditions for analogue switching.

Authors : Jonathon Cottom, Alexander Shluger
Affiliations : Department of Physics and Astronomy, University College London, UK

Resume : A TiN/SiO2/TiN stack is used as a model system to study the mechanisms of electroforming and reset is SiOx based resistive random access memory devices (ReRAM) using density functional theory (DFT) and atomistic modelling. The SiO2/TiN interface is constructed using DFT simulations assuming different degrees of hydroxylation of SiO2 surface. Trapping of two extra electrons at intrinsic sites inside SiO2 film results in weakening of Si-O bonds and emergence of efficient bond breaking pathways for producing neutral O vacancies and interstitial Oi(2-) ions with low activation barriers (≈ 0.2 eV) [1]. These barriers are further reduced at the TiN/SiO2 interface and by the electric field, facilitating diffusion of Oi(2-) from the bulk towards the interface. The charge transition level for the Oi (0/2-) moves towards that of the TiN Fermi level as the Oi approaches the interface resulting in a transfer of the electrons to the TiN electrode at the interface. An initial ‘oxidation’ of the interface takes place via the formation of a TiO layer at Ti-interface sites. The Ti-O bonds have high barriers (> 1.2 eV) for dissociation and O migration along the surface. Once the interface Ti-sites are occupied, Oi are incorporated at or in the layers directly below the interface or diffuse inside TiN via grain boundaries and desorb into gas [2]. Reset happens as the result of O trapped at the interface diffusing back into the oxide and recombining with O vacancies. Retention is facilitated by the high barrier to Ti-O bonds dissociation at the interface, which is reduced as the reset bias is applied. These results provide the first computational evidence for the mechanisms of oxygen trapping and release at the metal/oxide interface in resistive switching/reset processes. [1] D. Z. Gao, et al., Nanotechnology, 27(50), 505207 (2017) [2] A. Mehonic, et al., Adv. Materials, 28(34), 7486-7493 (2016)

Authors : Eszter Piros, Stefan Petzold, Alexander Zintler, Sankaramangalam Ulhas Sharath, Nico Kaiser, Robert Eilhardt, Tobias Vogel, Seyma Topcu, Eric Jalaguier, Emmanuel Nolot, Christelle Charpin, Leopoldo Molina-Luna, Christian Wenger, Lambert Alff
Affiliations : Institute for Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Germany; Institute for Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Germany; Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Darmstadt, Germany; Institute for Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Germany; Institute for Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Germany; Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Darmstadt, Germany; Institute for Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Germany; Institute for Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Germany; CEA, LETI, Grenoble, France; CEA, LETI, Grenoble, France; CEA, LETI, Grenoble, France; Advanced Electron Microscopy Division, Institute of Materials Science, Technische Universität Darmstadt, Darmstadt, Germany; IHP, Leibniz-Institut für innovative Mikroelektronik, Frankfurt (Oder), Germany; Institute for Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Germany;

Resume : Resistive Random Access Memories (RRAM) have enormous potential for neuromorphic applications because of their ability to tune the device resistance between multiple stable resistance states. RRAM based on transition metal oxides, such as HfO2 and Y2O3, is gaining increasing attention due to their superior performance, low energy consumption and their compatibility with CMOS technology. As resistive switching in these materials is based on anionic motion under an external electric field, defects have a big influence on the switching characteristics. To explore their role on device performance, defect engineered hafnia- and yttria-based RRAM devices were grown by molecular beam epitaxy. With the help of oxygen-engineering, the stoichiometry and hence the oxygen vacancy concentration of the functional layer can be controlled reproducibly. The device stoichiometry was found to have a great influence on the appearance of quantized conductance states for both hafnia and yttria, opening up the possibility to exploit quantum point contacts for synaptic applications. For the yttria-based devices, gradual switching dynamics were adjusted under bipolar DC operation, while the resistance was tuned through constant voltage amplitude nanosecond regime pulsing, qualifying these devices for multibit and neuromorphic applications. A transition from abrupt to gradual reset characteristics can be achieved in other material systems, e.g. in hafnia-based RRAM by substitution of trivalent La.

15:30 Coffee break    
Materials Concepts - RRAM : Christian Wenger
Authors : Hyunsang Hwang
Affiliations : Dept. of Materials Sci. & Eng. POSTECH, KOREA

Resume : ....

Authors : V. Bragaglia, A. La Porta, D. Jubin, M. Halter, Y. Popoff, F. Horst, D. Dávila, S. Abel, J. Fompeyrine, B.J. Offrein
Affiliations : IBM Research-Zurich Säumerstrasse 4 CH–8803 Rüschlikon Switzerland

Resume : Numerous electronic device concepts are currently under development to emulate the synaptic behavior for brain inspired computing. Resistive-RAM (RRAM) devices based on filamentary conduction in a nm-thick HfO2 layer are promising candidates due to their excellent potential in terms of low power consumption and scalability. Further improvements of the resistive switching parameters are important for this technology to be applied for neural network inference and training, such as a gradual resistance change and device reliability. A tight control of the electro-chemical reactions and dynamics of the oxygen ions at the HfO2 interfaces and across the layer is essential for a reliable change of the RRAM conductance. Ti/HfO2-based RRAM devices were extensively investigated, showing abrupt programming characteristic of the high resistive state (HRS) and low resistive state (LRS). In our study, we replace the Ti layer with a properly designed thin WO3 film acting as an oxygen exchange layer with the HfO2 film. The WO3 crystalline matrix is known to be a good ion conductor. Compared to the Ti/HfO2 RRAM, our WO3/HfO2-based RRAM results in a more gradual and tunable resistive state transitions upon programming biases. Detailed properties of our device will be discussed and compared to the Ti/HfO2 counterpart. We believe our work is a step forward towards better synaptic elements by exploring new material combinations that convert local filament switches into smoother volumetric ones.

Authors : Kuzmichev Dmitriy, Bokov Valentin, Markeev Andrey
Affiliations : Moscow Institute of Physics and Technologies

Resume : One of the most promising candidates for high-density storage class memory (SCM) are reputed metal-insulator-metal (MIM) resistive switching devices (ReRAM) due to theirs excellent scalability. In many reported systems resistance switching is related with the formation of the conductive filaments (CFs) in the insulator layer of the MIM structure, which usually revealed linear I-V characteristics in ”ON” state and required the electroforming step. In this switching mechanism random fluctuations of resistive switching (RS) parameters are inevitable. Besides there is one more notable problem arising during such RS elements integration into cross-bar arrays (CBA) , namely- the presence of sneak current paths between devices which lead to mistakes in reading operation. Hence, the development of forming-free non-filamentary RS cell can solve the problem of parameter fluctuations. Moreover, achieving of nonlinear or asymmetry current-voltage characteristics can suppress sneak currents and allow to create selector-free CBA. In this work we present non-filamentary forming-free RS devices based on single layer and bilayer dielectric with Ta2O5/Ta interface. The stacks revealed HRS/LRS (10^3), rectification ratio (~10^3) and area dependent RS. By modification of oxide thicknesses and voltage sweep rate, RS and rectifying properties of devices can be adjusted. Moreover, resistive switching effect in devices can be fully suppressed by temperatures lower than 260 Kelvin degrees.

Poster Session : Christian Wenger
Authors : Masato Araki, Tsuyoshi Hasegawa
Affiliations : Waseda Univ.

Resume : ‘Tug of war’ operation is an algorithm for parallel processing which is inspired by decision making a single cell amoeba does. We are implementing the function in molecular-gap atomic switches, in which Ag nanowires growing from an active electrode work as volume conserved ‘arms’ of an amoeba. In order to limit the amount of Ag atoms that precipitate from an active electrode, we employed a Ta2O5-Ag co-deposited film as the active electrode. Pt/PTCDA/ Ta2O5-Ag/Pt devices, where PTCDA is a molecular layer, were fabricated. In the co-deposition, we varied a composite ratio between Ta and Ag. One has a Ta/Ag ratio of 0.26, and the other has that of 0.99. The devices with the Ta/Ag ratio of 0.26 exhibited non-volatile operation, meaning that growing Ag nanowire stably existed after removing bias application. On the other hand, devises with the Ta/Ag ratio of 0.99 only exhibited volatile operation. For achieving the ‘Tug of war’ operation, an Ag nanowire should stably exist until another Ag nanowire grows in the other side. The Ta/Ag ratio for the ‘Tug of war’ operation should change depending on the thickness of a molecular layer, the volume of a Ta2O5-Ag electrode, etc. In the presentation, we will report the results of such systematic experiments.

Authors : Nozomi Mishima, Tsuyoshi Hasegawa
Affiliations : Waseda Univ.; Waseda Univ.

Resume : Silver sulfide has excess silver ions that does not make ionic bonds with sulfur ions but work as dopants, making silver sulfide semiconductor. Namely, the electronic conductivity of silver sulfide is determined by the number of excess silver ions contained in the crystal. Such excess silver atoms can be electrochemically removed using a scanning probe microscope. Using the technique, we confirmed that resistance of silver sulfide changes depending on the amount of excess silver atoms. In the experiment, we fabricated a silver sulfide nanodot, which is about 100 nm in diameter and 30 nm in height, using nanosphere lithography and vapor sulfurization techniques. Two metal leads are connected to a silver sulfide nanodot for measuring change in resistance while removal of excess silver atoms. AFM with a conductive cantilever was used, where bias was applied between a cantilever and a silver sulfide nanodot to remove excess silver atoms, while small bias was applied between two metal leads that sandwiched a silver sulfide nanodot to measure the change in resistance. As such, we confirmed that resistance increases with removing excess silver atoms. We also confirmed that resistance decreases when the removed silver atoms are re-dissolved into a silver sulfide nanodot. Namely, the phenomena are reversible. We believe that the technique has a potential to develop novel devices.

Authors : Wataru Hiraya, Tsuyoshi Hasegawa
Affiliations : Waseda Univ.

Resume : Precise control of ionic transfer has become more important in developing such as energy devices and next generation memory devices. Atom-by-atom control is the ultimate goal in some sense. Recently, control of layer-by-layer precipitation and dissolution of Ag atoms from an Ag2S nanodot was demonstrated, where discrete levels of electrochemical potential in an Ag2S nanodot was utilized. Namely, the number of precipitated/dissolved Ag atoms can be precisely controlled by applied bias. Since electrochemical potential can be defined to any materials, the technique should be other materials, not only the solid electrolyte. In this study, we applied the technique to an Ag-doped Ta2O5, which electronically changes from insulator to semiconductor depending on the amount of doped-Ag atoms remaining in Ta2O5. We fabricated an Ag-doped Ta2O5 nano-island by co-sputtering with an electron beam lithography patterning. Bias application and monitoring the precipitation/dissolution of Ag atoms were achieved using C-AFM. As a result, we succeeded in observing layer-by-layer growth/shrinkage of an Ag filament from a Ta2O5 nano-island, suggesting the technique can be applicable to any materials

Authors : Keita Ojima, Tsuyoshi Hasegawa
Affiliations : Waseda Univ

Resume : This study aims to realize reservoir computing using a network of Ag₂S-islands. Injection of a small amount of electrons as an input signal induces Ag nanowire growth from an Ag₂S island. When we have a Ag₂S-island network with multiple number of input/output electrodes, several conduction paths through Ag₂S islands are formed by the Ag nanowire growth. The network of the conduction paths that dynamically changes by input signals is expected to work as a reservoir. In this study, formation of a conductive path is observed using a C-AFM. Namely, input/output electrodes of Pt(40nm)/Ti(10nm) were formed on a SiO₂ substrate by EB-lithography. Then, we deposited Ag(2-3nm) on the SiO₂ substrate and sulfurized them in a sulfur vapor at elevated temperature, resulting in Ag₂S islands with a nanogap between them. Bias application between two electrodes formed a conductive path in the Ag₂S-island network, which was confirmed by a C-AFM measurement. Ag nanowire also shrinks depending on the conditions of input signals. These results suggest that Ag₂S-island network can work as a reservoir that dynamically changes in the computing.

Authors : T. Bentrcia12,*, F. Djeffal2 and H. Ferhati2
Affiliations : 2 LEPCM, University of Batna 1, Batna 05000, Algeria. 1 LEA, Department of Electronics, University of Batna 2, Batna 05000, Algeria. * Corresponding author: E-mail:

Resume : Our objective in this work is focused on the investigation of an adaptive neuro-fuzzy inference system for predicting the ageing of Double Gate (DG) TFET devices. Such trend is expressed by assessing the degradation of some performance criteria due to the presence of interface traps. In this context, the ION to IOFF ratio, the swing factor and small signal parameters are adopted to reveal the ageing phenomenon. The employment of ATLAS 2-D simulator allows the elaboration of the dataset to be used for the training of the neuro-fuzzy system. The obtained simulation outcomes show the good performance of the developed system for predicting the ageing degradation of the considered TFET design. Moreover, the proposed approach can play an important role to investigate TFET-based nanoelectronic circuits including the ageing-related degradation effects.

Authors : Chisato Arima1, Tohru Tsuruoka2, Yasuhisa Naitoh3, Hisashi Shima3, Hiroyuki Akinaga3, Tsuyoshi Hasegawa1
Affiliations : Waseda Univ. 1, NIMS2, AIST3

Resume : Molecular-gap atomic switch that operates by controlling the formation and annihilation of metal filament in a molecular layer between a solid electrolyte electrode and a counter metal electrode is expected to be used as a synaptic device in a brain-type computer. It is also expected to be used in a cosmic apace such as in a satellite. Actually, operations of gapless-type atomic switches are now examined in the satellite launched at January 2019. However, we found that a molecular-gap atomic switch has an issue in the operation in vacuum, which is the increase in the operating bias. In the worst case, operation is not available in vacuum. We thought that desorption of moisture both from a molecular layer and a solid electrolyte disables electrochemical reactions needed for the operation. Therefore, we decided to cover a switching area with an ionic liquid, which does not desorb even in vacuum. In the experiment, the ionic liquid-covered switch showed a stable switching with an operating bias almost same as that in air. The result suggests that the ionic liquid prevents the desorption of water molecules from the switching area. Moreover, it may supply water molecules to the device. In this study, we also reduced a switching time of a molecular-gap atomic switch by changing a substrate of the device. These experimental results will be presented.

Authors : Eszter Piros, Martin Lonsky, Stefan Petzold, Eric Jalaguier, Emmanuel Nolot, Christelle Charpin, Christian Wenger, Jens Müller, Lambert Alff
Affiliations : Institute of Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Darmstadt, Germany; Institute of Physics, Goethe-University Frankfurt, Frankfurt am Main, Germany; Institute of Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Darmstadt, Germany; CEA, LETI, Grenoble, France; CEA, LETI, Grenoble, France; CEA, LETI, Grenoble, France; IHP, Leibniz-Institut für innovative Mikroelektronik, Frankfurt (Oder), Germany; Institute of Physics, Goethe-University Frankfurt, Frankfurt am Main, Germany; Institute of Materials Science, Advanced Thin Film Technology, Technische Universität Darmstadt, Darmstadt, Germany

Resume : Resistive Random Access Memory (RRAM) is one of the most promising candidates for non-volatile memory and neuromorphic applications because of their high read- and write speed, excellent scalability, and tuneable resistance. One of the biggest challenges, however, is to improve device reliability and variability, which requires further insight into the charge transport mechanisms governing resistive switching. Therefore, we comparatively investigated the noise properties of oxygen engineered stoichiometric and highly oxygen deficient hafnia [1] by electronic fluctuation spectroscopy to explore the physical nature of conduction in both resistive states, finding a strong dependency on stoichiometry, voltage bias amplitude, and DC cycling. The investigations were carried out in both the time- and the frequency domains. The observation of multilevel- and anomalous random telegraph noise and corresponding Lorentzian spectra are also discussed. Our observations are consistent with the physical picture that higher oxygen vacancy concentration results in a broader distribution of the trap energies and their associated time constants. Additionally, the HRS noise was successfully suppressed through DC cycling for stoichiometric, as well as oxygen-deficient devices, which opens up the opportunity to reduce intra-device variability for both neuromorphic and memory applications. [1] S. U. Sharath, Adv. Funct. Mater. 27, 1700432 (2017)

Authors : A. A. Minnekhanov (1), A. V. Emelyanov (1,2), M. N. Martyshov (3), K. E. Nikiruy (1,2), B. S. Shvetsov (3), V. V. Rylkov (1,4), V. A. Demin (1,2)
Affiliations : 1) National Research Centre “Kurchatov Institute”, 123182 Moscow, Russia 2) Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Moscow Region, Russia 3) Lomonosov Moscow State University, 119991 Moscow, Russia 4) Kotel’nikov Institute of Radio Engineering and Electronics RAS, 141190 Fryazino, Moscow Region, Russia

Resume : Memristive devices are of great interest nowadays owing to a number of their attractive properties. The multilevel character of RS is one of the most important features of memristors for emulating synapses in neuromorphic systems (NSs), because it allows one to use local learning rules, for example “spike-timing-dependent plasticity” (STDP). It is also important that there are organic memristors that could be used in biomedicine. One of the most promising of them are structures based on parylene (PPX) due to the simple and cheap production of this polymer and its safety for the human body. However, their implementation in NSs has not been reported yet, and their mechanism of RS remains unclear. Therefore, the goal of this work was to shed light on these questions. We have studied memristors of Cu/PPX(100 nm)/ITO structure. It was found that the devices exhibit the advantages of low switching voltage (down to 1 V), long retention time (≥104 s) and multilevel RS (≥16 states). We have shown that the RS in the devices occurs by the electrochemical metallization mechanism. It was also found that PPX-based memristors can be trained by the STDP rules. Moreover, the model of classical conditioning was implemented as a simple NS using the samples. The obtained results demonstrate that the development of memristors based on PPX provides prospects for hardware realization of NSs for biomedical applications. This work was supported by the RSF (№ 18-79-10253) and RFBR (№ 18-37-20014).

Authors : Alice Selby, Loukas Michalas, Themis Prodromakis, Jessamyn Fairfield
Affiliations : School of Physics, National University of Ireland, Galway; Electronic Materials and Devices Research Group, Zepler Institute, University of Southampton; Electronic Materials and Devices Research Group, Zepler Institute, University of Southampton; School of Physics, National University of Ireland, Galway and CÚRAM;

Resume : Memristance in nanowire networks arises from each individual nanoscale junction changing in response to voltage stimuli, leading to an overall network change in conductivity dependent upon the number and weight of junctions. These devices can form multiple pathways for conducting current and can thus be used for neuromorphic emulation, with multiple conduction pathways mimicking ionic signalling pathways in neurons. Although these devices have demonstrated memristive I-V sweeps, there has been little study on the more biologically relevant pulsing of networks. Here we show that dropcast nanowire networks clearly demonstrate current-driven memristance with high on/off ratios, up to 10^6, along with nanosecond pulse response. Networks were shown to be electroforming friendly with one I-V sweep setting the network at low voltages, and memristance values enabling multistage memory. These devices are not driven by voltage, having a closer dependence on the current passing through the network. Nanowire network response to pulsed electrical stimuli in lateral electrode configurations allow for not only emulation of synapses but integration in future cross-disciplinary work, due to the exposed memristive interfaces.

Authors : D. Korolev, A. Belov, V. Lukoyanov, S. Gerasimova, M. Mischenko, A. Mikhaylov
Affiliations : Lobachevsky University, Nizhny Novgorod, 603950, Russia

Resume : One of the ways determining the development of modern neuromorphic systems is based on the possibility of creating simple electronic analogues of neuron and synapse using thin-film memristive devices and arrays. Synaptic plasticity is the main property of memristive devices which is used for the realization of learning rules in memristive neuromorphic systems. Experimental implementation of such systems requires both engineering the memristive device structure for obtaining reproducible switching parameters and reliable demonstration of plasticity of memristive devices connecting electronic neurons in specific spiking neural architectures. In this work, the synaptic plasticity is studied for the optimized multilayer memristive devices based on the yttria-stabilized zirconia (YSZ) and TaOx thin films by using different voltage and current pulse trains, simulated and experimentally generated electrical activity of FitzHugh-Nagumo and Hodgkin-Huxley artificial neurons. Theoretical and experimental investigation of the dynamics of two electronic neurons coupled by a memristive device exhibiting the effect of short-term and long-term synaptic plasticity has revealed a change in the conditions of synchronization of neurons depending on the weight of synaptic connection. The research is supported by Russian Foundation of Basic Research (18-29-23001).

Authors : Stefano Brivio, Sabina Spiga
Affiliations : CNR- IMM, Unit of Agrate Brianza, via C. Olivetti 2, 20864 Agrate Brianza, Italy

Resume : Resistive switching devices (RRAMs) have demonstrated analogue operation upon appropriate programming conditions, which can be exploited in several emerging applications, like synaptic electronics. However, a metrics for the assessment of the analogue operation is still lacking. In this work, we provide a methodical characterization of the conductance dynamics produced by identical pulses as a function of the programming parameters in an HfO2-based memristive device. We highlight that, in the analog regime, the conductance change per pulse is non-linear, state-dependent and slows down approaching the conductance boundaries, following a generalized soft-bound behaviour [1]. According to theoretical neuroscience results [2], the synaptic dynamics influence the learning performance of a Spiking Neural Network (SNN). For this reason, we simulate of a SNN based on the our RRAM experimental data and on equations derived from mixed digital-analogue and sub-threshold VLSI neuron circuits. The SNN implement a plasticity rule compatible with both spike timing and rate timing, which guarantees improved sensitivity to spatio-temporal correlations compared the usual STDP. We show that RRAM non-linear soft-bound dynamics results in improved memory lifetime of the SNN than linear synapses with similar resolution, in agreement with the theoretical neuroscience framework [2]. 1. J. Frascaroli et al., Sci. Rep. 8, 7178 (2018) 2. S. Brivio et al., Nanotech. 30, 015102 (2019)

Authors : E.R.W. van Doremaele, S. Kazemzadeh, Y. van de Burgt
Affiliations : University of Technology Eindhoven

Resume : Traditional computing systems are unable to capture the capability of the brain in real world information processing as evidenced by the anticipated end to Moore’s law. Organic materials have recently emerged as building blocks of neural processing [1] and possess basic forms of neuroplasticity and can emulate brain-like functionality at the device level [2]. The excellent characteristics of organic electronic materials, such as low energy operation and tunability, allows these materials to be used as a first step towards efficient neuromorphic computing systems [3]. Nevertheless, fully autonomous bioelectronic applications demand not only the acquisition of biological signals, but also local data processing, storage and the extraction of specific features of merit. As a fist proof of concept, here we will show a simple biosensor based on a neuromorphic array which can be trained to classify a model disease (e.g. cystic fibrosis). We use ion selective sensors that are able to detect physiological levels of potassium and chloride and serve as the input to the hardware-implemented neural network. This example paves the way for more complex trainable biosensors and shows the potential of adaptable neuromorphic devices in a biological environment. 1. van de Burgt et al. Nature Materials, 2017 2. Gkoupidenis et al. Advanced Materials, 2015 3. van de Burgt et al. Nature Electronics, 2018

Authors : Piotr Zawal(a, b), Tomasz Mazur(a), Maria Lis(a, c), Konrad Szaciłowski(a)
Affiliations : (a) Academic Centre for Materials and Nanotechnology AGH University of Science and Technology, Poland (b) Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Poland (c) Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Poland

Resume : In recent years, memristive devices gained a significant amount of interest in terms of neuromorphic computing. High operation speed, non-volatile passive memory, high scalability and low power consumption are the features that explain why more and more effort is put into the development of memristive devices. Recently, hybrid organic-inorganic perovskites (OIPs) have been thoroughly investigated as optoelectronic elements, photosensors and memristors. The unique properties of memristors – hysteresis loop in I-V scans and intrinsic non-volatile memory – allow these class of devices to efficiently emulate the behaviour of biological neurons. It has already been shown, that OIPs can emulate simple synaptic learning rules, such as spike-timing dependent plasticity, spike-rate dependent plasticity, metaplasticity and memory consolidation. The OIPs are particularly interesting in terms of their light-absorbing and optoelectronic properties. However, the optical properties of the OIP-based memristive devices haven’t been thoroughly investigated yet. Here, we present a unique optoelectronic behaviour of a layered structure consisting of methylammonium lead iodide OIP and heptazine quantum dots. The quantum dots introduce additional electron traps, therefore modulating the photocurrent response of the perovskite. We observed that the frequency of the incident light pulses can modulate the amplitude of the generated photocurrent. This effects can be interpreted in the field of neuronal responses to the repeating stimulus as learning, habituation and spike-rate dependent plasticity.

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Materials Concepts - FeRAM : Christian Wenger
Authors : Stefan Slesazeck
Affiliations : NaMLab gGmbH, Dresden, Germany

Resume : The polarization reversal in ferroelectric HfO2 films is adopted to store information in three distinct device classes. Depending on the stack composition different devices can be constructed from the very same ferroelectric layer - ferroelectric capacitors (FeCAP), ferroelectric field effect transistors (FeFET) and ferroelectric tunnel junctions (FTJ). That is, the electrical characteristics of this devices are strongly influenced by the whole material stack, rather than being dictated by the properties of the ferroelectric layer itself. In my talk I will discuss the different device concepts and their suitability for memory and for beyond-memory application such as logic-in-memory or neuromorphic computation.

Authors : S. Varotto, L. Nessi, S, Petrò, G. Nicosia, S. Cecchi, R. Calarco, R. Bertacco, C. Rinaldi
Affiliations : Dipartimento di Fisica, Politecnico di Milano, via Colombo 81, 20133 Milano, Italy Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy Paul-Drude-Institut für Festkörperelektronik, Hausvogteiplatz 5-7, 10117 Berlin, Germany

Resume : Phase Change Materials (PCM) and ferroelectric RAMs have been investigated to achieve chips with neuromorphic hardware capable of processing larger amounts of information. PCM are usually based on GeTe and GeSbTe. GeTe has recently gathered renewed interest for its ferroelectricity, whose control would enable a new regime of usage, including the non-volatile control of the spin transport [1]. Here we exploit GeTe thin films in their ferroelectric (FE) regime, by electrical gating. The switching of the polarization through voltage pulses is measured as resistance variation of metal/GeTe heterojunctions (M/GeTe). The modulation of resistance is due to the different local band bending induced by the screening of the polarization charge. Piezoresponse Force Microscopy correlated the microscopic distribution of FE domains with the electrical resistivity of the junction. The low-voltage control is provided by the coercive voltage (3-7 V), with modulation of resistivity up to 800%. The switching is robust, with endurance up to 105 cycles. The number of subsequent voltage pulses, their amplitude, and duration, as well as exploiting ferroelectric minor loops, enable a continuous distribution of intermediate resistive states. The memristive behavior of GeTe in its ferroelectric regime holds potential for the development of a technology platform for neuromorphic computing based on materials “on the shelf” of semiconductor industry. [1] C. Rinaldi et al., Nano Letters 18, 2751 (2018)

Authors : Yizhou Zhang, Yulin Feng, Chen Liu, Peng Huang, Mengqi Fan, Lifeng Liu, Xiaoyan Liu, Jinfeng Kang
Affiliations : Institute of Microelectronics, Peking University

Resume : Abstract: HfO2 based ferroelectric devices have attracted increasing attentions as a promising candidate for future information processing technology applications due to the excellent characteristics such as great scaling ability, good CMOS compatibility and high remnant polarization (Pr) [1-3]. It is critical to deeply understand the cycling behaviors and the correlated physical effects. In this work, the HfO2-based ferroelectric devices with the structure of TiN/Hf0.5Zr0.5O2/TiN were fabricated under different post annealing temperatures. The cycling behaviors of HfO2-based ferroelectric devices are measured under various operation schemes with different pulse waveforms. The measured datas show that the cycling behaviors during wake-up and fatigue processes are correlated with the pulse waveform. Meanwhile, the dependence of the cycling behaviors on the post annealing temperatures are also observed. These observed cycling behaviors are correlated with the generation and redistribution of oxygen vacancies in the Hf0.5Zr0.5O2 layer by using the physical mechanism [4]. References: [1] T. S. Böscke et al., “Ferroelectricity in hafnium oxide thin films”, Appl. Phys. Lett., vol. 99, no. 10, pp. 102903, 2011. [2] J. Müller et al., “Ferroelectricity in HfO2 enables nonvolatile data storage in 28 nm HKMG”, VLSIT 2012”, VLSIT 2012, pp. 25–26. [3] J. Müller et al., “Ferroelectric hafnium oxide: A CMOS-compatible and highly scalable approach to future ferroelectric memories”, IEDM 2013, 10.8. [4] C. Liu et al., “Role of Oxygen Vacancies in Electric Field Cycling Behaviors of Ferroelectric Hafnium Oxide”, IEDM 2018, 16.4.

Authors : M. Halter, A. Chanthbouala, É. O’Connor, F. Eltes, Y. Popoff, M. Sousa, S. Abel, V. Garcia, J. Fompeyrine
Affiliations : Neuromorphic devices & systems, IBM Research GmbH - Zurich Research Laboratory, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland, and Integrated Systems Laboratory, ETH Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland; Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay, Palaiseau 91767, France; Neuromorphic devices & systems, IBM Research GmbH - Zurich Research Laboratory, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland, Currently at EPFL, Lausanne, Switzerland; Neuromorphic devices & systems, IBM Research GmbH - Zurich Research Laboratory, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland, and EMPA - Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dübendorf, Switzerland; Neuromorphic devices & systems, IBM Research GmbH - Zurich Research Laboratory, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland; Neuromorphic devices & systems, IBM Research GmbH - Zurich Research Laboratory, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland; Neuromorphic devices & systems, IBM Research GmbH - Zurich Research Laboratory, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland; Unité Mixte de Physique, CNRS, Thales, Univ. Paris Sud, Université Paris-Saclay, Palaiseau 91767, France; Neuromorphic devices & systems, IBM Research GmbH - Zurich Research Laboratory, Säumerstrasse 4, CH-8803 Rüschlikon, Switzerland;

Resume : The emergence of ferroelectricity in doped HfO2 has attracted a great deal of attention since its discovery in 2011. Among the various compositions, HfxZr1-xO2 (HZO) films have emerged as one of the more promising material. The ferroelectricity in thin doped HfO2 films is generally accepted to originate from the crystallization of a non centro-symmetrical orthorhombic phase. Here we report the utilization of millisecond Flash Lamp Annealing (ms-FLA) for the stabilization of this ferroelectric phase, down to a thickness of 3 nm. The combination of a 120 s long preheat step of 375°C with a 20 ms flash lamp pulse results in ferroelectric HZO films with TiN electrodes. This statement is supported by x-ray diffraction (XRD), polarization hysteresis and piezo force microscopy (PFM) measurements. In 10 nm and 5 nm thick HZO layers a remanent polarization (Pr) of ~21 µC/cm2 and ~15 µC/cm2 is achieved, respectively. Cycling analysis shows an increase of endurance for the ms-FLA compared to rapid thermal annealing (RTA) by one decade, up to 107 bipolar cycles. An SiO2 encapsulation further increases the endurance to 108 cycles. For the 3nm thin HZO sample, the preheat temperature of the ms-FLA was increased to 550°C to successfully crystallize HZO in the orthorhombic phase. On the contrary with standard RTA anneals, no conditions could be found for 3nm thick films without a fraction of the layer crystallized in the monoclinic phase. To our knowledge, this is also the first time XRD data show clear evidence for the orthorhombic phase in 3nm films. We also demonstrate the advantage of this approach using other electrodes than TiN. This shows the benefits of a fast thermal process to stabilize ultra-thin ferroelectric films, which are promising candidates for non-volatile memory and neuromorphic hardware applications when used as tunnel junctions.

10:30 Coffee break    
Materials Concepts- Polymers : Martin Ziegler
Authors : V. Erokhin
Affiliations : Institute of Materials for Electronic and Magnetism, Italian National Council of Research (IMEM-CNR) Parma, Italy

Resume : Organic memristive device was designed for mimicking some properties of synapses. We will consider several applications of these devices for the realization of perceptrons, hardware realization of models of parts of animal nervous systems, frequency dependent plasticity, and STDP learning. Finally, a results of the use of these devices for connecting two neuronal cells from the rat cortex will be discussed.

Authors : Sreetosh Goswami, T. Venkatesan
Affiliations : National University of Singapore, National University of Singapore

Resume : Artificial intelligence (AI) has been heralded as the flagbearer of the fourth industrial revolution. But it comes with a cost and that is computing power. It is projected that by 2040, we will need more computing energy than the total energy we can produce at the moment. So, we need devices that can offer higher computing/ storage density with low energy consumption. We are addressing these challenges using a molecular-electronic route. Historically, organic electronic devices have stimulated scientific excitements in optoelectronics, especially OLEDs but are yet to make any other significant technological impact. The reasons behind their limited success are their poor robustness, stability, endurance and most importantly, the lack of mechanistic understanding that restricts the emergence of approaches to solve these problems. We have overcome each of these difficulties in our memristors based on transition metal complexes of azo-aromatic ligands that exhibit high reproducibility (~350 devices), fast switching (?30 ns), excellent endurance (~10^12 cycles), stability (>10^6 s) and scalability (down to ~60nm2)[1, 2]. Using in-situ Raman spectroscopy we are able to track the electronic changes in molecules in-operando at every point of our voltage sweep providing a clear picture of our molecular mechanism that enables us to do different molecular and device engineering to achieve targeted functionalities. Using devices of this genre we are addressing the existing computing challenges via three routes, (i) By designing devices with ultra-low power: We are able to design memristors with switching voltage as low as 70mV, with energy ~36aJ/ 60nm^2. The current and voltage levels of these devices meet the requirements specified in ITRS road map. (ii) By designing memristors and memcapacitors with multiple discrete plateaus: We have developed memristors with 3- 4 distinct conducting plateaus which also shows mem-capacitance. Their concomitant occurrence is enabled by symmetry breaking of our film-molecules driven by voltage, a new paradigm in condensed matter physics. (iii) Brain-inspired computing: Using an unprecedented self-assembly of our molecular films we are able to realize discrete conductance plateaus in a memristor with sharp switching. We are able to demonstrate up to 29 well defined, reproducible, discrete states each of which are characterized by in-situ Raman. We are exploring the potential of this system to enable synaptic functionalities. References 1. Goswami S, Matula AJ, Rath SP, Hedström S, Saha S, Annamalai M, et al. Robust resistive memory devices using solution-processable metal-coordinated azo aromatics. Nature materials 2017, 16(12): 1216. 2. Valov I, Kozicki M. Non-volatile memories: Organic memristors come of age. Nature materials 2017, 16(12): 1170.

Authors : Yu Rim Lee, Nae-Eung Lee
Affiliations : Yu Rim Lee; Department of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea Nae-Eung Lee ; 1Department of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea 2SKKU Advanced Institute of Nano Technology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea 3Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea 4Institute of Quantum Biophysics (IQB), Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea 5Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea

Resume : Recently, bio-inspired sensory devices are attracted a great attention, however, still they are limited in challenges in emulating the preprocessing abilities of sensory organs such as reception, filtering, adaptation, and sensory memory at the device level itself. Here, inspired by structure and parallel functions of reception and preprocessing the information of Merkel-cells, which form synapse-like connections with afferent neuron(Merkel-cell Neurite Complex, MCNC), we designed flexible and intrinsically intelligent tactile sensor. Using an organic synaptic transistor structure with ferroelectric nanocomposite gate dielectric of barium titanate nanoparticles and poly(vinylidene fluoride-trifluoroethylene), synaptic functions are demonstrated. Furthermore, tactile stimuli induces triboelectric-capacitive coupling effect and ferroelectric dipoles switching so that post synaptic current of this architecture is expressed as tactile information with synaptic functions. Adaptation, filtering and memory functions can be achieved in a single device without any extra components, with synaptic functions which also can be modulated by varying the nanocomposite composition. This work provides a new paradigm of intelligent sensor for artificial perception systems by imitation of synaptic functions extended on function of slowly adapting reception, with simple structure of Merkel-cell neurite complex.

Authors : Bobo Tian1,2,3, Brahim Dkhil1
Affiliations : 1Laboratoire Structures, Propriétés et Modélisation des Solides, CentraleSupélec, CNRS-UMR8580, Université Paris-Saclay, 91190 Gif-sur-Yvette, France. 2Key Laboratory of Polar Materials and Devices, Ministry of Education, East China Normal University, Shanghai 200241, China. 3National Laboratory for Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

Resume : Poly(vinylidene fluoride) (PVDF) homopolymer and its copolymers with trifluoroethylene (P(VDF-TrFE)) are robust organic ferroelectrics which can be easily deposited onto any solid substrates with a good Van der Waals interfacial bonding on a large scale. We demonstrate here that even single-layer thickness organic homopolymer thin films remain ferroelectric at room temperature and can be used for ferroelectric tunnel junctions (FTJs). A TJ array based on PVDF homopolymer is designed on a Si substrate. We evidence the intimate link between polarization switching and resistance switching in these 190 nm wide solid-state FTJs, with a resulting room temperature tunnel electroresistance of more than 1,000%.[1] Further, by covering the PVDF copolymers on 2D MoS2, the ferroelectric transistor is demonstrated as a regulable artificial synapse in which the purely electronic mechanism associating with ferroelectric domain dynamics results in a steerable resistive switching within more than 1000 intermediate resistance states and as large as 4 orders. Essential synaptic plasticity, such as the long-term potentiation and depression (LTP/D) and spike-timing dependent plasticity (STDP), were emulated in a single device. The energy consumption for each synaptic operation is estimated to be less than 1 fJ. The endurance behaviour implies a 10-years life of a device working continuously at the biological brain work-frequency of 10 Hz. These performance gains and efficiency in the CMOS-compatible organic ferroelectric synapses [2] open a path towards implementation of unsupervised learning in high-density memristive crossbar arrays. References [1] B. Tian et al., Nat. Commun. 7, 11502, (2016) [2] B. Tian et al., Adv. Electron. Mater. 1800600 (2019)

12:30 Lunch break    
Memristive Hardware : Christian Wenger
Authors : D. Ielmini, Z. Sun, G. Pedretti
Affiliations : Politecnico Milano Dipartimento di Elettronica, Informazione e Bioingegneria Italy

Resume : The rise of big data creates a demand for novel computing architectures capable of solving hard problems with a large number of variables directly within the memory. We have recently introduced a novel concept of solving matrix algebra problems within memory arrays, where computing (multiplication and summation) is done physically within a crosspoint array of analogue resistive switching memory (RRAM) devices with close-loop configuration. Here, I will present the concept and illustrate the various implication for solving real-life problems, such as page ranking and differential equation. Scaling opportunities and a comparison with alternate computing concept, including digital, photonic and quantum technologies, will be discussed.

Authors : Iosif-Angelos Fyrigos, Vasileios Ntinas, Georgios Ch. Sirakoulis, Panagiotis Dimitrakis
Affiliations : Democritus University of Thrace, Democritus University of Thrace - Universitat Politecnica de Catalunya, Democritus University of Thrace, NCSR Demokritos

Resume : Emerging materials, novel technologies and unconventional computing approaches are promising replacements of conventional general purpose processors for the demanding neuromorphic applications, as they provide application-specific solutions to the physical limitations introduced by the CMOS technology and von Neumann computers. In this work, the memristor technology is combined with the segmented crossbar architecture for the exploration of novel hardware accelerators for neuromorphic applications. This combination exploits the high computational performance and low power dissipation of memristor networks with the integration scaling of the segmented crossbar, where the sneak path problem is suppressed. As a case study, text recognition based on Artificial Neural Networks with the proposed approach is presented and is also validated through SPICE-level simulations.

Authors : Heidemarie Schmidt1,2,3, Nan Du1, Danilo Bürger1, Ilona Skorupa4, Ramona Ecke1, Stefan E. Schulz1
Affiliations : 1Fraunhofer-Institut für Elektronische Nanosysteme, Abteilung Back-End of Line, Technologie-Campus 3, 09126 Chemnitz, Germany; 2Faculty of physics, Friedrich-Schiller University of Jena Max-Wien-Platz 1, 07743 Jena, Germany; Leibniz-Institut für Photonische Technologien e.V. (IPHT), Albert-Einstein-Str. 9, 07745 Jena, Germany; 4Helmholtz-Zentrum Dresden-Rossendorf, Institute of Ion Beam Physics and Materials Research, Bautzner Landstraße 400, 01328 Dresden, Germany

Resume : Memristor technology will strongly influence the architecture of computer systems in the near future. Its potential in several application domains, e.g. in-memory information processing [1], neuromorphic computing [2], hardware cryptography [3], and machine learning makes it more than ever necessary to understand the underlying resistive switching mechanisms and to look for electroforming-free memristors. We have developed an electroforming-free bipolar and an electroforming-free unipolar memristor, namely BiFeO3 and YMnO3, respectively. Impedance data analysis and quasi-static test measurements on BiFeO3 [4] with mobile oxygen vacancies and substitutional Ti donors on Fe lattice sites reveal that the field-accelerated ion mobility constitutes a source of ultra-nonlinearity and that the redistribution of oxygen vacancies during the writing step causes a nonvolatile reconfiguration of the barrier height of the top and bottom electrode. Temperature dependent transport measurements on YMnO3 [5] with vortex states reveal that the density of vortex states is changed during the writing step. A large and small concentration of vortices sets the YMnO3 into low resistance state and high resistance state, respectively. We discuss how Fuzzy logics are realized with electroforming-free memristors and how this can be exploited for machine learning. [1] You et al., Adv. Funct. Mat. 24, 3357-3365, 2014. [2] Du et al., Front. Neurosci. 9, 227, 2015. [3] Du et al., J. Appl. Phys. 115, 124501, 2014. [4] Du et al., Phys. Rev. Applied 10, 054025, 2018. [5] Rayapati et al. Journal of Applied Physics 124, 144102, 2018.

Authors : Dovydas Joksas, Mark Buckwell, Wing H. Ng, Anthony J. Kenyon, Adnan Mehonic
Affiliations : University College London

Resume : Resistive random-access memory (RRAM) is one of the most promising candidates for the realisation of synaptic weights in physically implemented artificial neural networks (ANNs). The main concern is that this might not be feasible due to the non-idealities of RRAM devices. In our work, we simulate the inference accuracy of physical ANNs that are implemented using non-ideal devices and consider each non-ideality separately in order to evaluate their effect on ANN performance. We analyse the effects of ratio of the high and low resistance states (HRS/LRS) in proportional mapping scheme, devices not able to electroform or stuck in one of the resistance states, current/voltage non-linearities, non-linearity of resistance modulation using voltage pulses and device-to-device variability. We find that in realistic scenarios HRS/LRS ratio and device-to-device variations have the most significant impact on inference accuracy. By employing the concept of a committee machine, we show that all non-idealities can be dealt with by combining multiple non-ideal physical ANNs to produce a single output that results in significantly better inference accuracy. Although this method results in larger area and higher power consumption due to multiple ANNs used, its parallel nature improves the robustness of the system and does not increase the computation time.

15:30 Coffee break    
Neural networks : Martin Ziegler
Authors : D. Querlioz
Affiliations : CNRS Centre de Nanosciences et de Nanotechnologies Palaiseau, France

Resume : In-Memory Computing exploiting memristive devices is an exciting road for implementing highly energy efficient neural networks. This vision is however challenged by the variability inherent to memristive devices, as the efficient implementation of in-memory computing does not allow error correction. In this work, we fabricated and tested a differential HfO2-based memory structure and its associated sense circuitry, which are ideal for in-memory computing. We show that our differential approach achieves the same reliability benefits as error correction, but without any CMOS overhead. We also show that it can naturally implement Binarized Deep Neural Networks, a class of deep neural networks discovered in 2016, which can achieve state-of-the-art performance with a highly reduced memory and logic footprint with regards to conventional artificial intelligence approaches. Our system is fully satisfactory for image and EEG signal classification applications, and can achieve extremely high energy efficiency. Finally, we evidence how the extra reliability provided by the differential memory allows programming the devices in low voltage conditions, where they feature high endurance of billions of cycles, and showing a road for better exploiting the properties of Hafnium oxide based devices.

Authors : Xavi Marti, Marc Mateu Mateus
Affiliations : Institute of Physics ASCR, v.v.i., Cukrovarnick ́a 10, 162 53, Praha 6, Czech Republic; Department of Electronic Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain

Resume : Magnetic materials have played a major role in information technologies. However, the word ‘magnetic’ refers in practice to one only class of magnetic materials known as ferromagnets which covered most of the data storage needs and are gradually entering random access memories, information processing and, more recently, neuromorphic computing. Antiferromagnets were discovered nearly contemporaneously with the transistor but despite being awarded a Nobel Prize in its Nobel Prize lecture, Louis Neel, admitted he found an interesting “but useless” magnetic order. Thus, antiferromagnets fell to academic research and only its auxiliary role in spin-valves brought some rather limited spotlight on them. In the past decade, antiferromagnets have been reborn while trying to improve spintronics effects on ferromagnets. It was found that most of the effects observed with ferromagnets had counterparts in antiferromagnets eventually with larger effects. Antiferromagnets started replicating many of the spintronics concepts based on ferromagnets and, by now, have established as an independent, mature, research field with some features that have been found to be exclusive for antiferromagnets. My contribution will focus on the recent most developments on the usage of antiferromagnetic materials on the field of neuromorphics. We will present the design of a completely analog spiking neuron build upon an antiferromagnetic thin film surrounded by standard CMOS transistors.

Authors : Bin Shi, Nicola Calabretta, Ripalta Stabile* (*presenting person)
Affiliations : Institute for Photonic Integration, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

Resume : Deep neural network architectures have shown their superior performance in visual classification, audio recognition, signal recovery, astronomy information processing, etc. Their intrinsic parallel computation scheme can boost computation speed and reduce power consumption. Electronic neuromorphic computing has already been developed to reduce processor power consumption. However, the computing speed is limited by the interconnection electronic bandwidth. We propose to use photonic computation as an alternative to pushing computing speed further while exploiting brain-inspired architectures. We employ an Indium Phosphide (InP) based photonic neural network to demonstrate an all-optical two-layer feed-forward neural network. A non-linear semiconductor optical amplifier (SOA) is used to generate the optical nonlinear activation function: it is possible to change the shape of the nonlinear function by tuning the driven current and the power ratio of the CW control laser to the optical input signal. The combination of the on-chip photonic cross-connect for weighted addition implementation and the off-chip optical nonlinear function allows to process 10Gbit/s data sequences with a normalized root mean square error of 0.21 at the very final output, which is comparable to using an E/O conversion approach after each layer. This result paves the way to implement a reconfigurable all-optical deep neural network with photonic integrated circuits with co-integrated optical amplifiers.

Authors : M. Salverda, R. Hamming-Green, C.P. Quinteros, P. Nukala, W.R. Acevedo, D. Rubi, S. Farokhipoor, B. Noheda
Affiliations : Zernike Institute for Advanced Materials, University of Groningen, 9747 AG, The Netherlands; Zernike Institute for Advanced Materials, University of Groningen, 9747 AG, The Netherlands; Zernike Institute for Advanced Materials, University of Groningen, 9747 AG, The Netherlands;Zernike Institute for Advanced Materials, University of Groningen, 9747 AG, The Netherlands; INN-CNEA and CONICET, Av. Gral Paz 1499, San Martin (1650), Buenos Aires, Argentina; INN-CNEA and CONICET, Av. Gral Paz 1499, San Martin (1650), Buenos Aires, Argentina; Zernike Institute for Advanced Materials, University of Groningen, 9747 AG, The Netherlands; Zernike Institute for Advanced Materials, University of Groningen, 9747 AG, The Netherlands, CogniGron Center, University of Groningen, 9747 AG, The Netherlands;

Resume : Self-assembled conductive and memristive networks could play a role in future cognitive computation, for example in a winner-takes-all system or for reservoir computing. In epitaxial thin films of orthorhombic, antiferromagnetic TbMnO3 grown on SrTiO3, domain wall networks exist with domain walls that are perfectly vertical and atomically thin. In these walls, every other terbium atom is replaced by a manganese atom, leading to a chemical environment that is quite different from that of the domains and displaying ferromagnetic interactions localized at the walls. As it happens in other oxide thin films with ferroelastic domain walls, it is expected that the electrical properties of the walls also deviate from those of the bulk. Most often the conductivity enhancement is due to defect migration and local reduction of the Schottky barrier close to the domain walls. In the case of TbMnO3 on SrTiO3, the presence of ferromagnetism (below 40K) at the walls suggests an increase of their metallicity. Here we investigate the electrical properties of these films after growing them on conducting Nb-doped SrTiO3 substrates. The interface between the TbMnO3 film and the conductive substrate forms a p-n junction. We fabricate macroscopic 2-terminal and 4-terminal geometry electrodes to measure DC I-V curves and we fit the data to the Shockley diode equation including a resistance in series. We use a model for the resistance of the films, based on domain wall density, film thickness and other structural parameters, to obtain a value of the sheet resistance of these walls.

Authors : Claudia Lenk (1), Lars Seeber (1), Stefanie Gutschmidt (2), Martin Ziegler (1)
Affiliations : (1) Technische Universität Ilmenau, 98693 Ilmenau, Germany (2) University of Canterbury, 8140 Christchurch, New Zealand

Resume : Acoustic sensing in the biological cochlea incorporates information processing at the sensor level, i.e. the hair cells, using the active dynamiccharacteristics of outer hair cells. These enable adaption of sensing properties to the actual hearing environment to improve frequency resolution, dynamic range and signal-to-noise-ratio and enable a selective amplification/suppression of signals, selected by attention focus or related to dangerous situations. We develop artificial hair cells based on micro-electromechanical systems, which provide sensing and neuromorphic pre-processing functionalities for acoustic signals. Therefore, our sensors are driven by a feedback algorithm into a nonlinear dynamics regime (near a bifurcation). Additionally, output-signal coupling is introduced to (i) increase the frequency range and (ii) enable nonlinear dynamics of the ensemble of coupled oscillators. Both regimes, the individual nonlinear oscillator as well as the coupled nonlinear oscillators, are used to realize a biomimetic processing. In particular, coupling increases the sensors response and shifts the system’s frequency range not only for the active but also for the passive case, thus providing an energy-efficient method for sensor adaptation. Due to the integrated filtering capability, the adaptable amplification and the operation principle based on coupled oscillators, this system can improve sensing technologies by reducing processing requirements and improving energy efficiency.

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09:00 Plenary Session (Main Hall)    
12:30 Lunch break    
Neuromorphic computing : Christian Wenger
Authors : Louis Andreoli(1), Stephane Chretien(2), Maxime Jacquot(1), Laurent Larger(1), Daniel Brunner(1)
Affiliations : (1) FEMTO-ST, UMR CNRS 6174, Univ. Bourgogne Franche-Comté, 25030 Besançon, France (2) National Physical Laboratory, Teddington, Middlesex TW11 0LW, UK

Resume : We have recently succeeded in the implementation of a large scale recurrent photonic neural network hosting up to 2025 photonic neurons. All net-work internal and readout connections are physically implemented with fully par-allel technology. Based on a digital micro-mirror array, we can train the Boolean readout weights using a greedy version of reinforcement learning. We find that the learning excellently converges. Furthermore, it appears to possess a conven-iently convex-like cost-function and demonstrates exceptional scalability of the learning effort with system size.

Authors : M. Lederer, T. Kämpfe, T. Ali, K. Seidel
Affiliations : Fraunhofer IPMS - Center Nanoelectronic Technologies (CNT)

Resume : Hardware based neuromorphic computing, which requires a synaptic memory capable of retaining a multitude of addressable conductance states, opens a possibility to bypass the von Neumann bottleneck [1]. Ferroelectric field effect transistors (FeFETs) based on doped hafnium oxide have been demonstrated as viable candidates for neuromorphic synapses [2]. Here, the multitude of remnant polarization levels can be used to modulate the drain current. By utilizing any of three distinct types of signal sequences, the different levels can be addressed. Those sequences are described by a varying number of pulses, pulse width, or pulse height, respectively [3]. The resulting conductance response (CR) of the synapse can be described by a set of parameters: Abruptness, conductance variation and conductance dynamic range [4]. Here, we present the CR modulation capability of HfO2 based FeFETs. On the one hand, influences from the signal sequence and read gate voltage were investigated. In case of the former, the pulse width and amplitude were varied, whereas latter is demonstrated to vary the abruptness of depression and potentiation in opposed directions. On the other hand, influences resulting from the dopant, interface layer, and other device integration related quantities were investigated. Furthermore, endurance was investigated in regard to neuromorphic application. Additionally, it was demonstrated, that a CR with a nonlinearity coefficient smaller than 0.1 can be achieved for read voltages as low as 0.3V. [1] Indiveri, G. et al.; Proc. IEEE, 1379-1397, 2015 [2] Jerry, M. et al.; IEDM, 6.2.1-6.2.4, 2017 [3] Oh, S. et al.; IEEE Electron Device Lett. 38 (6), 732-735, 2017 [4] Gi, S.-G. et al.; IEEE Trans. Electron Devices 65 (9), 3996-4003, 2018

Authors : George Alexandru Nemnes (1,2), Daniela Dragoman (1,3)
Affiliations : (1) University of Bucharest, Faculty of Physics, Materials and Devices for Electronics and Optoelectronics Research Center, 077125, Magurele-Ilfov, Romania; (2) Horia Hulubei National Institute for Physics and Nuclear Engineering, 077126, Magurele-Ilfov, Romania; (3) Academy of Romanian Scientists, Splaiul Independentei 54, Bucharest, 050094, Romania.

Resume : Motivated by the increasing interest in reconfigurable and neuromorphic computing architectures, we propose a reconfigurable logic gate able to reproduce several functionalities: OR, AND, XOR and CNOT [1]. This is achieved by manipulating the spin states in a double quantum wire with a coupling window enabled by a Rashba field. The ballistic spin polarized transmission functions are calculated using an effective mass scattering formalism applied to multi-channel, multi-terminal systems. For a given geometry and potential map of the embedded system, the device functionality can be switched by the external input only, set by the gate voltage, which tunes the Rashba coupling. Several configurations of the device region are investigated. Furthermore, different configurations of input and output terminals are considered. This approach shows that the proposed spintronic devices can be integrated in programmable architectures, implementing both classical and quantum algorithms. [1] G. A. Nemnes and Daniela Dragoman, Physica E 111, 13 (2019).

Authors : Setareh Kazemzadeh1, Scott T. Keene2, Claudia Lubrano3, 4, Armantas Melianas2, Yaakov Tuchman2, Giuseppina Polino3, Lucio Cinà5, Francesca Santoro3, Alberto Salleo2, Yoeri van de Burgt1
Affiliations : 1 Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612AJ Eindhoven, The Netherlands 2 Department of Materials Science and Engineering, Stanford University, USA 3 Center for Advanced Biomaterials for Healthcare, Istituto Italiano di Tecnologia, Italy 4 Dipartimento di Chimica, Materiali e Produzione Industriale, Università di Napoli Federico II, Italy 5 Cicci Research, Italy

Resume : With the development of organic low power and biocompatible neuromorphic systems, novel paradigms based on integrating artificial neuronal network and machine learning with local adaptive biological systems can be envisioned. This approach could have applications in a variety of smart “trainable” devices, notably in adaptive prosthetics. To achieve a feedback mechanism between the biological systems and neuromorphic devices, the first critical step is understanding a complete spectrum of neuron to neuron communication. Current brain implants only focus on the recording of electrical signals from the action potentials of excited neurons. However, neuron to neuron communication is dominant by chemical recognition of neurotransmitter molecules. In this study, we succeed to bridge this gap for the first time by creating a neurotransmitter-sensitive organic neuromorphic device which directly translates chemical signals to a change in the neuromorphic memory state. Memory state of our neuromorphic device alters proportionally to different rates of dopamine secreted by PC12 neuron-like cells at the pre-synaptic. Applying voltage pulses at the pre-synaptic electrode drive the oxidation of dopamine at the electrolyte interface, leading to a change in synaptic weight at the post-synaptic electrode similar to short term and long term potentiation.

15:30 Coffee break    
Neuromorphic Computing and Materials Concepts : Martin Ziegler
Authors : H. Jaeger
Affiliations : Jacobs University Bremen gGmbH Germany

Resume : The term "computing" has a specific, firm, powerful, traditional meaning -- condensed in the paradigm of Turing computability (TC). A core aspect of TC is the perfectly reliable composition of perfectly identifiable symbolic tokens into complex, hierarchical symbolic structures. But all which is novel and promising and original in "neuromorphic" information processing leads away from such perfect symbolic compositionality. Apparently new formal conceptions of "computing" would be most welcome (and a new term for it, too). In my talk I will carve out a number of concrete aspects that separate neuromorphic information processing from symbolic computing - some of them classical topics in the philosophy of AI, others having more recently emerged from technological progress in non-digital hardware.

Authors : Finn Zahari1, Felix Georg2, Julian Strobel3, Julia Cipo2, Sven Dirkmann4, Sven Gauter2, Jan Trieschmann4, Richard Marquardt1, Lorenz Kienle3, Thomas Mussenbrock4, Martin Ziegler5, Holger Kersten2, Hermann Kohlstedt1
Affiliations : 1 Nanoelectronics, Faculty of Engineering, Kiel University, Germany; 2 Plasma Technology, Department of Physics, Kiel University, Germany; 3 Synthesis and Real Structure, Faculty of Engineering, Kiel University, Germany; 4 Electrodynamics and Physical Electronics, Electrical Engineering and Information Science, BTU Cottbus-Senftenberg, Germany; 5 Department of Micro- and Nanoelectronic Systems, TU Ilmenau, 98693 Ilmenau, Germany

Resume : Neuromorphic analogue systems attract considerable attention to realize novel bio-inspired computing architectures. Circuits with advantageous features such as lower power dissipation and cognitive capabilities are main research goals. Double barrier memristive devices (DBMD) with the layer sequence Nb/Al/Al2O3/NbO𝑥/Au are promising candidates to emulate synaptic behavior in analog circuits. Selector-device free crossbar-arrays based on DBMDs have been already realized for pattern recognition tasks. The recognition performance of such systems strongly depends on the individual electrical I-V characteristics of the DBMDs and the deposition method of the layers, e.g. thin film magnetron sputtering. In this contribution, we show evidence that crucial parameters of the process plasma, such as floating potential, electron temperature as well as particle and energy flux at the substrate, are strongly correlated with I-V characteristics of the individual devices. These results are supported by transmission electron microscopy (TEM) and kinetic Monte Carlo simulations. Our findings enable a new pathway for the development of plasma engineered memristive devices.

Authors : Stefano Brivio, Jacopo Frascaroli, Erika Covi, Sabina Spiga
Affiliations : CNR - IMM, Unit of Agrate Brianza, via C. Olivetti 2, 20864 Agrate Brianza, Italy

Resume : Variability and noise are considered both an issue and an opportunity for hardware neural systems, depending on the target application. Indeed, deep learning hardware accelerators require high precision and reliability; in turn, variability and noise are fundamental, to some extent, for bio-inspired intelligent systems. Therefore, in view of the real deployment of the potential of RRAM devices for neural applications, their variability and noise characteristics must be investigated and understood. To this aim, we evidence that filamentary RRAMs produce telegraphic noise features of ionic origin when stimulated by trains of identical pulses.[1] The latter is the programming scheme of choice for RRAM neural applications. The observed zero-average telegraphic resistance variations are directly produced by pulses. The persistence of the resistance state after the delivery of a pulse is much longer than what is expected for conventional random telegraph noise (RTN). Indeed, RTN is dominated by charge trapping and release occurring within few tens of ms. In turn, the evidenced random resistance changes are stable up to 1 s, indicating that the phenomenon has an ionic origin. A model describing the stimulated noise amplitude as a function of the average resistance value is developed. The model considers a dynamic equilibrium condition at the interface between the conductive filament responsible for the switching and its interruption. [1] Brivio et al Scie. Rep. 9 6310 (2019)

Authors : K.E. Nikiruy, A.V. Emelyanov, I.A.Surazhevskiy, V.V. Rylkov, A.V.Sitnikov, V.A. Demin
Affiliations : NRC Kurchatov Institute

Resume : Memristors as resistors with memory attract considerable attention due to their ability to mimic synapses in hardware neuromorphic systems (NS). Memristors based on nanocomposite (NC) (CoFeB)x(LiNbOy)100–x are good in this respect due to their high endurance, long retention time, and spike-timing-dependent plasticity (STDP). Resistive switching effect in such systems could be explained by conductive filaments formation and destruction caused by the electromigration of oxygen vacancies. NC film was synthesized by ion-beam sputtering in N2 ambient. The metal phase content x was ~6 at. %. Fabricated samples show excellent memristive characteristics: endurance >106 cycles, Roff/Ron > 100, retention time > 104 s, precision of resistive state programming better than 0.5%. We have observed a STDP behavior of NC memristors and experimentally demonstrate a simple way for the STDP window shape modulation by introducing the coefficients controlling the neuron spike amplitudes. In such a way the STDP window shape could be modulated from a classical asymmetric form to a bell-shaped, as well as to anti-STDP and to anti-bell-shaped. The possibility of STDP learning of NC memristor connected via analogue neurons based on op-amps was also demonstrated. Obtained results could be used for bio-inspired operation of future large memristor-based NS with associative and reinforcement learning ability. This work was supported by the Russian Science Foundation 16-19-10233.

18:00 Graduate Student Awards Ceremony & Reception 18:00-21:00 (Main Hall)    
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New Materials for Neuromorphic Computing I : Sabina Spiga
Authors : Giueseppe Piccolboni
Affiliations : Weebitnano Bâtiment de Haute Technologie, Bat. 52, 7 Prv Louis Neel 38000, Grenoble France

Resume : In the vast flash-replacement emerging memory landscape Oxide-based resistive RAM (ReRAM) have been one of the most promising candidates in the last years and many works were published on the subject. Weebit-Nano is a company focused on SiOx-based ReRAMs. SiOx is a promising material thanks to its high band-gap, that allows to attain a clear separation among the resistance states, and its temperature stability that grants data In the vast flash-replacement emerging memory landscape Oxide-based resistive RAM (ReRAM) have been one of the most promising candidates in the last years and many works were published on the subject. Weebit-nano is a company focused on SiOx-based ReRAMs. SiOx is a promising material thanks to its high bandgap, that allows to attain a clear separation among the resistance states, and its temperature stability that grants data retention over time. In addition to its promising physical properties, SiOx is full CMOS compatible and offers high manufacturability. Finally, SiOx thickness and stoichiometry are easily tunable. By working on SiOx engineering and top electrode optimization we were able to tune the initial resistance of our devices (Rinit) and to correlate it with the maximum high resistance state (HRS) resistance. Measurements showed how there's a Rinit window around 100 [MΩ] that grants the highest possible HRS resistance and hence the best separation among memory states. A high enough memory window could also pave the way to the capability of encoding more than only 2 memory states between the low resistance state (LRS) and the higher HRS.

Authors : Gianluca Milano, Luca Boarino, Ilia Valov, Carlo Ricciardi*
Affiliations : Gianluca Milano: 1 Applied Science and Technology Dep., Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy 2 Center for Sustainable Future Technologies, Istituto Italiano di Tecnologia (IIT), 10129, Torino, Italy; Luca Boarino: 3 Nanoscience and Materials Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Strada delle Cacce 91, 10135 Torino, Italy; Ilia Valov: 4 Institut fur Werkstoffe der Elektrotechnik II, RWTH Aachen University, 52074 Aachen, Germany. 5 PGI-7 (Electronic Materials), Research Centre Juelich, 52425 Juleich, Germany; Carlo Ricciardi: 1 Applied Science and Technology Dep., Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy *Corresponding author Email:

Resume : Self-assembled metal oxide nanowires (NWs) represent fascinating building blocks to artificially emulate human brain functionalities such as learning and memory, when their electronic properties are coupled with ionic effects [1,2]. Moreover, these low-dimensional systems offer the possibility to achieve new device functionalities by exploiting surface/quantum effects or by applying external stimuli such as illumination [2]. In this work, we propose a single crystalline ZnO NW as a model system to investigate ionic-related phenomena underlaying resistive switching and neuromorphic functionalities of ECM memristive cells. Indeed, by properly selecting materials and operating conditions, the single NW devices exhibited all memristive/neuromorphic functions such as non-volatile bipolar memory with multilevel capability, volatile switching and synaptic function through the emulation of Ca2 dynamics of biological synapses [3]. A detailed analysis of the physical mechanism of switching revealed the importance of interfaces in crystalline materials, since it was directly observed that ionic migration is restricted on the NW surface. Moreover, we show that ionic contribution from ambient moisture can modulate not only electronic properties of these devices but can also regulate the ionic transport mechanism. References [1] H.G. Manning et al. Nat. Commun. 9 (1), 3219, 2018 [2] G. Milano et al. Adv. Elect. Mater 1800909, 2019 [3] G. Milano et al. Nat. Commun. 9 (1), 5151, 2018

Authors : A. S. Goossens, T. Banerjee
Affiliations : [1] University of Groningen, Zernike Institute for Advanced Materials, The Netherlands [2] Groningen Cognitive Systems and Materials Center, University of Groningen, the Netherlands

Resume : Memristors, non-linear devices of which the microscopic internal state can be modified and measured as a change in the conductance, can be used to emulate elements of the human brain, for example when used as synapses, they show synaptic plasticity through their ability to modulate the strength of connecting pathways. For this purpose Schottky junctions of ferromagnetic metals on Nb-doped SrTiO3, where memristive behaviour originates from electric field driven interfacial changes, are studied at room temperature. This behaviour is observed in as-fabricated devices, eliminating the need of an unfavourable electro-forming step. The resistive switching in these devices is gradual, allowing for well-controlled multilevel switching [1]. Some of the most important parameters influencing the resistive switching behaviour, including magnitude of applied voltage, time and number of applied pulses, are investigated. Different pulse widths and shapes gives rise to differences in the state-retention characteristics: in particular, longer lasting memory effects are realised by longer stimuli, which can be used to mimic learning and forgetting in short-term memory. Accumulative effects and how presenting multiple consecutive stimuli influences the overall state of the device are also investigated. The retention after the application of multiple pulses, in particular after a sequence of two pulses, shows that the last pulse in a sequence has the strongest influence on the observed retention characteristics. The direction of resistance change depends on the polarity of this final pulse, but earlier pulses are also shown to have a non-negligible influence. This could be used as a short term memory and to recall the last digit in a sequence. Application of a single negative (positive) pulse or sweep is seen to give rise to an increase (decrease) in resistance. When, on the other hand, repeat pulses of one polarity are sequentially applied, the observed changes in resistance are gradually enhanced. Using a power-law model, the positive pulsing action can be well-described. This modelling allows for the development of synaptic weight change learning rules, which can be used for training. Both for consecutive sweeping and pulsing measurements it is observed that the first event induces the largest resistance change and subsequent events give rise to increasingly smaller changes: this is reminiscent of the learning processes in the brain, where the largest change occurs during the first learning event. References [1] A. S. Goossens, et al., J. Appl. Phys., 124(15), 152102, 2018.

Authors : A.G. Kvashnin, H.A. Zakaryan,Yu.A. Kvashnina, A.R. Oganov
Affiliations : Skolkovo Institute of Science and Technology, Skolkovo Innovation Center 121205, 3 Nobel Street, Moscow, Russia, Moscow Institute of Physics and Technology, 141700, 9 Institutsky lane, Dolgoprudny, Russia; Yerevan State University, 1 Alex Manoogian St., 0025, Yerevan, Armenia; Skolkovo Institute of Science and Technology, Skolkovo Innovation Center 143026, 3 Nobel Street, Moscow, Russia; Skolkovo Institute of Science and Technology, Skolkovo Innovation Center 121205, 3 Nobel Street, Moscow, Russia, Moscow Institute of Physics and Technology, 141700, 9 Institutsky lane, Dolgoprudny, Russia, International Center for Materials Discovery, Northwestern Polytechnical University, Xi'an, 710072, China;

Resume : Operation of many of the industrial application is not possible without using superhard materials. Nowadays it is important to search for new cheap and effective materials which can substitute traditional materials in many field of science and technology. Traditionally material can be called as superhard if its Vickers hardness is higher than 40 GPa [1–3]. Here we predict new tungsten and molybdenum borides, some of which are promising hard materials that are expected to be thermodynamically stable in a wide range of conditions. We computed the composition-temperature phase diagram, which shows the stability ranges of all predicted phases. New boron-rich compound WB5 is predicted to be superhard with Vickers hardness of 45 GPa and at the same time it possesses high fracture toughness of ~4 MPa·m0.5 [4]. Newly predicted tungsten and molybdenum boride (WB5 and MoB5) are found to be thermodynamically stable in a wide range of temperatures at ambient pressure. Temperature dependences of the mechanical properties of WB5 were studied using quasiharmonic and anharmonic approximations. Our results suggest that WB5 and MoB5 remains a high-performance material even at very high temperatures. The work was supported by Russian Science Foundation (Grant No. 17-73-20038). Reference [1] V. L. Solozhenko, S. N. Dub, and N. V. Novikov, Diam. Relat. Mater. 10, 2228 (2001). [2] V. L. Solozhenko and E. Gregoryanz, Mater. Today 8, 44 (2005). [3] V. L. Solozhenko, O. O. Kurakevych, D. Andrault, Y. Le Godec, and M. Mezouar, Phys. Rev. Lett. 102, 015506 (2009). [4] A. G. Kvashnin, H. A. Zakaryan, C. Zhao, Y. Duan, Y. A. Kvashnina, C. Xie, H. Dong, and A. R. Oganov, J. Phys. Chem. Lett. 3470 (2018).

10:30 Coffee break    
New Materials for Neuromorphic Computing II : Sabina Spiga
Authors : Zhansong Geng, Christian Ziebold, Sebastian Thiele, Miroslav Mikolasek, Juraj Breza, Jörg Pezoldt, Martin Ziegler, and Frank Schwierz
Affiliations : Technische Universität Ilmenau, Technische Universität Ilmenau, Technische Universität Ilmenau, Slovak University of Technology Bratislava, Slovak University of Technology Bratislava, Technische Universität Ilmenau, Technische Universität Ilmenau, Technische Universität Ilmenau

Resume : Recently, memristors have attracted considerable attention in the electronic devices community. Originally the memristor has been considered as the fourth fundamental two-terminal circuit element that provides a link between charge and magnetic flux, in addition to the resistor (link between current and voltage), the capacitor (link between voltage and charge), and the inductor (link between current and magnetic flux). Over the years, however, the definition of the memristor has changed, and today any two-terminal device showing a pinched hysteresis loop in its current-voltage characteristics is called memristor. Memristors are of particular interest since they possess an inherent memory effect and are able to emulate the function of biological synapses. In the present paper, we report on the fabrication and characterization of lateral few-layer MoS2 memristors as a specific type of memristive device. Our memristors consist of mechanically exfoliated MoS2 flakes transferred to a SiO2/Si substrate and two Ti/Au contacts serving as source and drain. The electrical characterization under different ambient conditions (air, N2 atmosphere, vacuum) reveals that both the shape of the hysteresis and the absolute current values strongly depend on the ambient. Moreover, it is shown that bare devices without protection (i.e., without a passivation layer) seriously degrade during operation, particularly in ambient air.

Authors : Dr. Niloufar Raesi-Hosseini Dr. Christos Papavassilioiu
Affiliations : Imperial College London, Department of Electronics and Electrical Engineering

Resume : The prompt development of artificial intelligence systems and future advanced robotics incentives evolving demand on the understanding of the neuromorphic mechanism. Memristor is the fourth fundamental circuit element with a great opportunity for high-performance neuromorphic computing. We introduce a simple metal-insulator-metal system using a bioinspired material for memristor-based artificial synapse and demonstrate its behaviors. We emulated the physiological behavior of biological synapses by the evolution of the memristive device with external pulses. The plasticity of the artificial synapse is achieved, and it is comparable with the natural brain system. We represent a bioinspired neuromorphic device which is derived from a natural and waste material. The introduced memristor is capable to compete with its inorganic counterparts. We used an easy and inexpensive solution process to fabricate devices on a plastic substrate under ambient conditions. The electronic synapse is composed of collagen with magnesium electrodes that emulate synaptic functions by engineering the input pulses. In nature, neurons are communicated via synapses. Like a biological synapse (Figure 2), the top and bottom electrodes are regarded as the presynaptic and postsynaptic terminals, while the collagen thin film and cations are considered as the synaptic cleft and neurotransmitters, respectively. SEM image of the fabricated device confirms the well-deposited film and the desired structure of the neuristor. We have verified using a natural polymer to fabricate a biocompatible artificial synapse with flexibility, robustness, and analog conductance characteristics. The important synaptic characteristics including potentiation and depression, excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP) has been realized by the fabricated neuristor.

Authors : S. P. Ioannou1, E. Kyriakides1, O. Shcneegans2 and J. Giapintzakis1
Affiliations : 1)Department of Mechanical and Manufacturing Engineering, University of Cyprus, 75 Kallipoleos Av., PO Box 20537, 1678 Nicosia, Cyprus; 2)Group of Electrical Engineering of Paris (GeePs) UPMC and Paris-Sud Universities, CNRS, Centrale Supélec, Gif-sur-Yvette, France

Resume : Neuromorphic computing, which emulates synaptic functions of biological neural networks, is emerging as one of the most viable successors of current computing paradigms. The use of established thin film deposition techniques, the safeguarding of Si compatibility, along with the precise control of synthesis, processing and characterization of multifunctional oxide films, is expected to mitigate current CMOS technology bottlenecks, through the production of two-terminal synaptic devices, structured in crossbar arrays. This work investigates the memristive/synaptic behavior and underlying mechanism of thin film cells based on LiCoO2-cathode in combination with various anode materials such as SiO2, SiO2/TiO2 and a:SiO2/poly-Si on p-doped Si substrates. For the fabrication of the multilayer thin film cells Pulsed Laser Deposition, conventional and reactive RF Sputtering have been employed. Depending on the anode material and thin film structure, reversible Li-ion migration from the LiCoO2 cathode, give rise to separate memristive functionalities such as binary or analog resistive switching, based on filamentary Li bridging and homogeneous insulator to metal transition (IMT) of LiCoO2 respectively. Additionally synaptic plasticity functions such as Paired Pulsed Facilitation (PPF) and Spike Timing Dependent Plasticity (STDP) along with the inherent stochastic feature of these devices disclose great potential for future spiking neural network applications.

Authors : Woon-Oh Choe, Ho Won Jang
Affiliations : Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea.

Resume : Two dimensional materials such as graphene and hexagonal boron nitride have atomically thin structure. They can also stack and form a Van der Waals layered structure. For their atomic thickness, they have been studied a lot for the application to various electronic devices such as field effect transistors, photoelectric catalysts, and so on. Transition Metal Dichalcogenides (TMD) are one of the most popular two dimensional materials system for their various mechanical and electrical properties. Among them, MoS2 is the most familiar one for its abundance with stability and semiconducting band gap. However, TMD materials with Van der Waals layered structure have low electronic and ionic conductivity between the layers. Here we demonstrate resistive switching with vertically aligned MoS2 to minimize the loss of conductivity by the formation and rupture of conduction filament. Chemical Vapor Deposition (CVD) method is used for the synthesis of vertically aligned MoS2. Also Ab-initio Density Functional Theory (DFT) calculation is performed to verify the mechanism of conduction filament based on sulfur vacancies.


Symposium organizers
Beatriz NOHEDAUniversity of Groningen

Zernike Institute for Advanced Materials, Nijenborgh 4, 9747AG Groningen, The Netherlands
Christian WENGERIHP GmbH

Leibniz Institut fuer innovative Mikroelektronik, Im Technologiepark 25, 15236 Frankfurt/Oder, Germany
Martin ZIEGLERTU Ilmenau

Microelectronic and Nanoelectronic Systems, Gustav-Kirchhoff-Str., 98684 Ilmenau, Germany

Unit of Agrate Brianza, via C. Olivetti 2, 20871 Agrate Brianza (MB), Italy