Impact of Device Materials on Neuromorphic Circuits

Facing the rapid development and energy-efficiency demands of artificial intelligence and neuromorphic computing, traditional von Neumann architectures struggle with storage and power consumption bottlenecks. Neuromorphic devices, as next-generation computing technologies, have been validated using machine-learning software and CMOS hardware, but still face issues in power consumption and learning speed. This paper introduces four distinctive device mechanisms. Zein-based memristive synapses combine biocompatibility with mechanical flexibility, offering a naturally sustainable solution for wearable neuromorphic systems. Low-dimensional nanomaterials exploit quantum confinement and van der Waals heterojunctions to realize ultra-low-energy plasticity and gate-tunable multistate storage, outperforming conventional CMOS in integration density and energy efficiency. YO memristors, analyzed via coupled electro-thermal modeling, demonstrate that surface roughness and yttrium oxygen-reservoir layers strongly modulate conductive-filament stability and switching voltages, providing quantitative guidelines for nanoscale interface engineering. WO-based electrochemical random-access memory leverages oxygen-rich/oxygen-poor phase separation to achieve non-volatile retention under short-circuit conditions, satisfying the requirements of low-power inference chips. These material pathways collectively drive neuromorphic hardware toward brain-like, scalable, and ultra-low-power computation.