Investigation of Neural Networks Using Synapse Arrays Based on Gated Schottky Diodes

Abstract

A synapse device array based on the gated Schottky diodes (GSDs) is fabricated. This GSD operates in reverse mode, so the synapse current is considerably low, helping to implement a low-power hardware-based neural networks (HNNs). The reverse Schottky diode current, which represents the synaptic weight, is modulated by applying program or erase pulses. In this GSD, the reverse diode current is used as the synapse current, but the forward diode current is cut off. This is an important feature to prevent the sneak path problem in the crossbar array. A synapse array consisting of fabricated 200 GSDs shows a variation (s/mu) of 0.34, 0.22, and 0.14 for three different synaptic weight states. By using this GSD array, we perform the vector-by-matrix multiplication, and evaluate the inference accuracy of MNIST. As a baseline accuracy for MNIST classification, a convolutional neural network similar to Lenet-5 is designed and gives an accuracy of 99.53%. Normalization method is applied to the weights trained in the network to map the weights into the conductance range of synapse device. The adaptive weight quantization is then applied to the normalized weights. We verify that the HNN using GSDs works well in comparison to the baseline network even in the presence of non-ideal characteristics of synapse devices.