Publication date: 22nd July 2026
The rapidly growing energy demand associated with artificial intelligence and data-intensive technologies is accelerating the search for novel hardware paradigms capable of overcoming the intrinsic limitations of conventional von Neumann computing architectures. In this context, neuromorphic devices, which emulate key features of biological information processing, are emerging as promising candidates for next-generation, energy-efficient computing systems.
A central challenge in the field is the development of materials and device architectures capable of emulating synaptic functionalities such as memory, plasticity, and stimulus-dependent conductance modulation. While oxide-based memristive systems have a relatively high maturity, increasing attention is now being directed toward materials that can couple electrical and optical stimuli, enabling multimodal operation.
In this presentation, we discuss recent progress in the exploration of neuromorphic behavior and switching mechanisms in halide perovskites and low dimensional semiconductors. These material platforms offer a promising route toward adaptive, low-power, and optoelectronically tunable neuromorphic devices.
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