AUTHOR=Shen Yang , Tian He , Liu Yanming , Wu Fan , Yan Zhaoyi , Hirtz Thomas , Wang Xuefeng , Ren Tian-Ling TITLE=Modeling of Gate Tunable Synaptic Device for Neuromorphic Applications JOURNAL=Frontiers in Physics VOLUME=9 YEAR=2021 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.777691 DOI=10.3389/fphy.2021.777691 ISSN=2296-424X ABSTRACT=

The emerging memories are great candidates to establish neuromorphic computing challenging non-Von Neumann architecture. Emerging non-volatile resistive random-access memory (RRAM) attracted abundant attention recently for its low power consumption and high storage density. Up to now, research regarding the tunability of the On/Off ratio and the switching window of RRAM devices remains scarce. In this work, the underlying mechanisms related to gate tunable RRAMs are investigated. The principle of such a device consists of controlling the filament evolution in the resistive layer using graphene and an electric field. A physics-based stochastic simulation was employed to reveal the mechanisms that link the filament size and the growth speed to the back-gate bias. The simulations demonstrate the influence of the negative gate voltage on the device current which in turn leads to better characteristics for neuromorphic computing applications. Moreover, a high accuracy (94.7%) neural network for handwritten character digit classification has been realized using the 1-transistor 1-memristor (1T1R) crossbar cell structure and our stochastic simulation method, which demonstrate the optimization of gate tunable synaptic device.