Dynamic Memristors for Neuromorphic Computing

As semiconductor technology enters the more than Moore era, there exists an apparent contradiction between the rapidly growing demands for data processing and the visible inefficiency rooted in traditional computing architecture. Neuromorphic systems hold great prospect in enabling a new-generation of computing paradigm that can address this issue, which demands device components with rich dynamics and nonlinearity. In this talk, we demonstrate optoelectronic synapse based on van der Waals ferroelectric α-In₂Se₃ with controllable temporal dynamics under both electrical and optical stimuli, and the tight coupling between the two distinct physical processes gives rise to heterosynaptic plasticity with light tunable relaxation timescale. A multimode reservoir computing system with adjustable nonlinear transformation function is in turn demonstrated, showing enhanced network performance, and the tunable timescale in α-In₂Se₃ synapse enables multiscale signal processing in parallel sub-reservoirs with significantly improved computing accuracy. The inherent conductance drift of phase change memory is exploited as physical decay function to realize in-memory eligibility trace, demonstrating excellent performance during reinforcement learning in various tasks. The spontaneous in-memory decay computing and storage of policy in the same phase change memory give rise to significantly enhanced energy efficiency compared with traditional graphics processing unit platforms. Incorporating dynamics in memristors represents the future way to empower computing.