Innovations for neuromorphic computer: insulators, semiconductors and superconductors for base elements of brain-inspired neural networks

Abstract

With the growing energy demands of modern computers based on the von Neumann architecture, the need for more energy-efficient computing solutions has become increasingly urgent. This challenge has stimulated a shift toward non–von Neumann computing paradigms, particularly those inspired by the brain’s neural network architecture. Artificial neural networks (ANNs), which emulate the structure and function of biological neurons and synapses, have gained significant attention in this context. These networks require innovative functional nanostructures designed using advanced insulating, semiconducting, and superconducting materials, offering the potential for dramatic reductions in energy consumption and addressing key limitations of conventional von Neumann systems.