Gate-Controlled Memristors and their Applications in Neuromorphic Architectures

Abstract

We discuss the theory of gated memristive devices, which exhibit continuous states over three orders of magnitude and can be programmed independently of reading. A model is generated by using knowledge of the device physics and fitting the parameters to measured data. The gate-controlled memristor simplifies the implementation of analog artificial neural network architectures significantly. Using this, a very simple architecture is presented, along with a simulation and its performance metrics. The simulated analog neural neural network is able to achieve 88.9 percent accuracy on the MNIST test set. The objective is to demonstrate the advantages that gated memristors can give to analog neural networks.