Configurable Synaptic and Stochastic Neuronal Functions in ZnTe-Based Memristor for an RBM Neural Network

Abstract

This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two-terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low-current level (mu A) in the forming process, a stable memory-switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired-pulse facilitation/depression, potentiation/depression, spike-amplitude-dependent plasticity, and spike-number-dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high-current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free-drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model. An Ovonic threshold switching material based multifunctional memristor device is presented. The two-terminal multifunctional memristor is expected to function as a selector in memory arrays, a synaptic device and a stochastic neuron in neuromorphic systems by setting the appropriate operating current level in the forming process. Utilizing the stochastic neurons, a restricted Boltzmann machine model is created. image