Wide-Bandgap Semiconductor-Based Neuromorphic Computing

Abstract

ABSTRACT Neuromorphic computing has emerged as a promising paradigm to overcome the energy inefficiency and data-transfer bottlenecks of conventional von Neumann architectures by emulating the parallel and adaptive information processing of biological neural systems. To date, most neuromorphic hardware has relied on silicon-compatible or narrow-bandgap materials, which often face intrinsic trade-offs among operating voltage, thermal stability, endurance, and multifunctionality. Wide-bandgap semiconductors (WBGSs)—including Group III nitrides, gallium oxide, silicon carbide, and diamond—provide an alternative material platform enabled by their large bandgaps, strong polarization effects, diverse defect states, and compatibility with electronic and optoelectronic device architectures. This review surveys recent progress in WBGS-based neuromorphic computing, with an emphasis on material-enabled device physics rather than isolated demonstrations. Typical device concepts, including memristors, synaptic transistors, and neuronal devices, are systematically discussed together with their underlying resistive switching, charge trapping, polarization modulation, and optoelectronic mechanisms. Strategies for device integration and performance benchmarking are also addressed. Finally, remaining challenges and future research directions toward scalable and energy-efficient neuromorphic systems based on WBGSs are outlined.