ICNCE 2026

Information technology is undergoing a profound transformation: traditional, command-based data processing and CMOS technology, in which algorithms act like recipes to produce deterministic results, are increasingly being complemented and, in some areas, replaced by AI-based approaches. These approaches rely on machine learning and the automated extraction of knowledge from data, yielding results that are inherently probabilistic. Data processing in such systems is based on artificial neural networks (ANNs), inspired by neuroscience, whose operation relies on threshold-like computational elements and artificial synapses. A key challenge of this development is the rapidly growing energy demand of modern computing systems.
In our projects, we investigate memristive devices and systems for neuromorphic computing (NC) that have the potential to operate far more energy-efficiently than conventional computer architectures. The focus is placed on the development and analysis of redox-based memristive elements.
The lecture outlines the physical principles underlying these devices and illustrates how materials science and electrical engineering draw inspiration from the remarkable energy efficiency of the human brain—a topic intensively studied in neuroscience. Insights gained from this research are incorporated into the development of future computer hardware, which can subsequently be exploited in computer science and AI applications. We will demonstrate how such an interdisciplinary approach—bridging neuroscience, materials science, electrical engineering, and computer science—can make a decisive contribution to the realization of energy-efficient neuromorphic systems.
In addition, selected applications of AI will be presented, and their opportunities and risks at both the individual and societal levels will be briefly discussed. Finally, we will reflect on the question of whether future AI systems could develop some form of consciousness, thereby bringing the discussion back to the field of brain research.

Speaker:
Rainer Waser received his PhD in Physical Chemistry from the Technical University of Darmstadt in 1984. He subsequently joined the Philips Research Laboratories in Aachen before being appointed Professor at the Faculty of Electrical Engineering and Information Technology at RWTH Aachen University in 1992. In 1997, in parallel with his university appointment, he assumed the position of Director of the Institute for Electronic Materials at Forschungszentrum Jülich. His scientific work focuses on the physicochemical fundamentals of functional oxide materials — initially on their dielectric and ferroelectric properties, and later in particular on redox-based resistive switching phenomena. His research made substantial contributions to the development of memristive devices, which are now regarded as key candidates for non-volatile memory technologies, computing-in-memory architectures, and neuromorphic hardware systems. Together with Professor Matthias Wuttig and Regina Dittmann, he coordinated the Collaborative Research Center SFB 917 on “Resistively Switching Chalcogenides for Future Electronics,” funded by the German Research Foundation (DFG) from 2011 to 2023 and involving 14 institutes. In 2014, he received the Gottfried Wilhelm Leibniz Prize awarded by the DFG for his interdisciplinary contributions to the physics and materials science of emerging memory technologies. In 2019, he initiated the BMBF-funded structural transformation project “Neuro-inspired Artificial Intelligence Technologies for the Electronics of the Future in the Rhineland Region (NEUROTEC)” and – together with Max Lemme – he organized the ICNCE 2024.