Homologous gradient heterostructure‐based artificial synapses for neuromorphic computation
Changjiu Teng, Qiangmin Yu, Yujie Sun, Baofu Ding, Wenjun Chen, Zehao Zhang, Bilu Liu, Hui–Ming Cheng
Abstract
Abstract Gradient heterostructure is one of fundamental interfaces and provides an effective platform to achieve gradually changed properties in mechanics, optics, and electronics. Among different types of heterostructures, the gradient one may provide multiple resistive states and immobilized conductive filaments, offering great prospect for fabricating memristors with both high neuromorphic computation capability and repeatability. Here, we invent a memristor based on a homologous gradient heterostructure (HGHS), comprising a conductive transition metal dichalcogenide and an insulating homologous metal oxide. Memristor made of Ta–TaS x O y –TaS 2 HGHS exhibits continuous potentiation/depression behavior and repeatable forward/backward scanning in the read‐voltage range, which are dominated by multiple resistive states and immobilized conductive filaments in HGHS, respectively. Moreover, the continuous potentiation/depression behavior makes the memristor serve as a synapse, featuring broad‐frequency response (10 −1 –10 5 Hz, covering 10 6 frequency range) and multiple‐mode learning (enhanced, depressed, and random‐level modes) based on its natural and motivated forgetting behaviors. Such HGHS‐based memristor also shows good uniformity for 5 × 7 device arrays. Our work paves a way to achieve high‐performance integrated memristors for future artificial neuromorphic computation. image