Compact Modeling of Single Electron Memory Based on Perceptron Designs

Abstract

In this work, we present a Single electron random access memories based on perceptron designs used as the basic artificial bio-inspired neural processing element. The operation principles are described and illustrated for the first time by simulations results. Combining the Monte Carlo method with a direct solution of the stationary master equation, we use SIMON simulator and MATLAB for training process. The main goal of this work is to build a multilayer neural network used in recognition and classification using single electron devices. We further provide a write/Erase/ Read states chronogram to provide the key element of our work which is the charge stored in output neuron’s quantum dots.