Material-related problems of neuromorphic technologies

Advancements in materials science have been instrumental in the progression of neuromorphic technologies. However, existing neuromorphic hardware often relies on rigid and energy-intensive fabrication processes. There is an urgent need for innovative materials and device architectures that can bridge this gap while ensuring scalability and eco-friendliness.

Scope:

The inevitability of a revolution in electronic technologies is dictated by the emergence of new paradigms of electronics which cannot be implemented on existing hardware. Traditional computing architectures struggle with energy efficiency, scalability, and adaptability, particularly as AI and data-intensive applications demand greater computational power. In the past decades, electronics and telecommunications have become a new global energy consumer. Machine learning tends, in the limit, to consume all the power produced in the world, and this development model is costly, inefficient and unsustainable. Neuromorphic computing, inspired by biological neural networks, presents a promising alternative by enabling real-time, low-power processing. Crucial for this development are materials being able to implement bio-inspired functions. However, existing neuromorphic hardware often relies on rigid and energy-intensive fabrication processes, limiting material flexibility, integration potential, and environmental sustainability. There is an urgent need for innovative materials and device architectures that can bridge this gap while ensuring scalability and eco-friendliness.

Thus, the symposium is dedicated to the material problems of neuromorphic technologies and aims to bring together a wide range of theorists and experimentalists, software and hardware developers to exchange ideas and advances in this field.

Hot topics to be covered by the symposium:

• Theory and modelling
• Novel neuromorphic materials, cells and arrays
• Neuromorphic devices based on 0D, 1D and 2D materials
• Memristors for neuromorphic computing
• Resistive switching mechanisms
• Experimental techniques
• Manufacture technologies
• Electrical conduction
• Ion transport
• Neural networks and bio-interfaces
• Quantum neuromorphic systems
• Memcomputing
• Physical reservoirs for reservoir computing
• Applications of AI to neuromorphic materials research
• Optoelectronic memristive hardware
• Reliability of neuromorphic devices
• Multifunctional devices
• Defects in neuromorphic materials and devices
• Brain-inspired metamaterials

Invited Speakers:

• Sabina Spiga, Institute for Microelectronics and Microsystems, Agrate Brianza, Italy - tba
• Wilfred van der Wiel, University of Twente, The Netherlands “Reconfigurable Nonlinear Processing in Silicon”
• Dmitri Strukov, University of California Santa Barbara, USA "High-order Hopfield neural networks and Boltzmann machines and their application to combinatorial optimization problems"
• Ilia Valov, Jülich Research Centre and RWTH Aachen University, Germany “Electrochemical Polishing – how to control the resistance levels in memristive devices”
• Yuriy V. Pershin, University of South Carolina, Columbia, USA “Liquid state memristors for in-memory and neuromorphic computing”
• Hirofumi Tanaka, Kyushu Institute of Technology, Fukuoka, Japan “Material Intelligence: Material Physical Reservoir Devices for Highly Efficient Intelligent Systems”
• Regina Dittmann, Jülich Research Centre, Germany - tba
• Adrian Ionescu, Swiss Federal Institute of Technology in Lausanne - EPFL, Switzerland “Neuromorphic memristive sensors based on vanadium dioxide”
• Stephan Menzel, Jülich Research Centre, Germany “Temporal correlation effects in memristive devices on the sub-ns scale”
• John Heron, University of Michigan, Ann Arbor, USA "Picosecond scale switching and transient behaviour in epitaxial transition metal oxides for high-performance neuromorphic computing"
• Alexander Vahl, Leibniz-Institute for Plasmascience and Technology, Greifswald, Germany “Material Aspects of Resistive Switching in Nanogranular Matter”
• Bernardo Spagnolo,Palermo University, Italy - tba
• Simas Račkauskas, Kaunas University of Technology, Lithuania "ZnO nanotetrapod networks for neuromorphic computing"
• Lambert Alff, Technical University of Darmstadt, Germany “Materials requirements for the transition from digital to analog memristors”
• Stefan Wiefels, Jülich Research Centre, Germany - tba
• João Ventura, University of Porto, Portugal “Resistive switching in 2D MXenes: from fundamental studies to neuromorphic properties”
• Vasileios Bilalis, Technical University of Denmark, Lyngby, Denmark “Solid-State Redox as a Design Principle: Intertwining Heterostructure and Function in Complex Oxides for Neuromorphic Electronics”

Scientific committee members:

• Rainer Waser,RWTH Aachen University, Germany.
• Qiangfei Xia,University of Massachusetts, Amherst, USA.
• Gert Cauwenberghs, University of California San Diego, USA. 
• Philip Wong, Stanford University, USA. 
• András Halbritter, Budapest University of Technology and Economics, Hungary.
• Catherine Schuman,Oak Ridge National Laboratory, USA.
• Dietmar Fey,Friedrich Alexander University of Erlangen-Nürnberg, Germany.
• Alon Ascoli,Chua Memristor Center Dresden, Germany.
• Hans Hilgenkamp, University of Twente, The Netherlands.