Demonstration of Unsupervised Learning With Spike-Timing-Dependent Plasticity Using a TFT-Type NOR Flash Memory Array

Abstract

We investigate the characteristics of a synaptic imitation device using a thin-film transistor (TFT)-type NOR flash memory cell with a half-covered floating gate. The long-term potentiation (LTP) and long-term depression (LTD) required for the operation of the spike-timing-dependent plasticity (STDP) algorithm are implemented using the proposed pulse scheme. Unsupervised learning is successfully demonstrated by applying the STDP learning rule through software MATLAB simulation reflecting the LTP/LTD characteristics of the fabricated TFT-type NOR flash memory array. We present the learning and recognition processes of $28\times28$ MNIST handwritten digit patterns. First, STDP learning in a single-neuron string ( $784\times1$ ) is investigated, after which STDP learning is demonstrated in a multineuron array ( $784\times10$ ) with a lateral inhibition function to demonstrate the ability of multipattern learning and recognition. Meanwhile, we investigate the key factors of STDP unsupervised learning. Finally, an approach is suggested to implement a hardware neural network using the conventional CMOS technology for STDP unsupervised learning as a visual pattern recognition system.