Self‐Compliant, Variation‐Suppressed Memristor Implemented with Carbon Nanotube/hBN/Silver Nanowire Cross‐Point Structure
Jiayang Hu, Baini Li, Hailiang Wang, Yu Kang, Yuda Zhao, Yang Xu, Enzheng Shi, Yunfan Guo, Kai Xu, Bin Yu
Abstract
Abstract Implementing memristors for neuromorphic computing demands ultralow power, suppressed variations, and compact structure. Previously reported artificial neurons are mostly demonstrated with micrometer size in which the stochastic formation/rupture of conductive filaments leads to significant temporal and spatial variations. Additionally, external current compliance is commonly applied to ensure volatile switching behavior, inevitably increasing design complexity and power consumption due to auxiliary circuitry. Here, an ultra‐scaled volatile memristor is demonstrated using carbon nanotube (CNT)/hBN/silver nanowire (Ag NW) cross‐point structure with a conducting area of only 120 nm 2 . Owing to the nanoscale geometry for ion migration, the memristor exhibits suppressed cycle‐to‐cycle and device‐to‐device variations. Self‐compliant memristive behavior is achieved, simplifying the overall system design. Furthermore, the power consumption of the cross‐point memristor‐based neuron is drastically reduced. The results provide guidelines for tailoring the critical electrical behavior of geometry‐scaled memristor, generating practical understanding of ultra‐scaled memristor and its potential application in neuromorphic computing.