Strategic Development of Memristors for Neuromorphic Systems: Low-Power and Reconfigurable Operation

Abstract

The ongoing global energy crisis has heightened the demand for low-power electronic devices, driving interest in neuromorphic computing inspired by the parallel processing of human brains and energy efficiency. Reconfigurable memristors, which integrate both volatile and non-volatile behaviors within a single unit, offer a powerful solution for in-memory computing, addressing the von Neumann bottleneck that limits conventional computing architectures. These versatile devices combine the high density, low power consumption, and adaptability of memristors, positioning them as superior alternatives to traditional complementary metal-oxide-semiconductor (CMOS) technology for emulating brain-like functions. Despite their potential, studies on reconfigurable memristors remain sparse and are often limited to specific materials such as Mott insulators without fully addressing their unique reconfigurability. This review specifically focuses on reconfigurable memristors, examining their dual-mode operation, diverse physical mechanisms, structural designs, material properties, switching behaviors, and neuromorphic applications. It highlights the recent advancements in low-power-consumption solutions within memristor-based neural networks and critically evaluates the challenges in deploying reconfigurable memristors as standalone devices or within artificial neural systems. The review provides in-depth technical insights and quantitative benchmarks to guide the future development and implementation of reconfigurable memristors in low-power neuromorphic computing.