Unsupervised Learning by Spike-Timing- Dependent Plasticity in a Mainstream NOR Flash Memory Array—Part II: Array Learning

Abstract

In this article, we show that, by exploiting the program and erase conditions identified in Part I of this article, operating a mainstream NOR Flash memory array as an artificial synaptic array learning in an unsupervised manner according to the spike-timing-dependent plasticity (STDP) rule can be easily achieved. To this aim, first, a word-line and a bitline voltage scheme allowing cells to change their memory state and, in turn, their synaptic weight to mimic the STDP learning rule is presented. Then, a simple spiking neural network making use of the NOR Flash array as a synaptic array is discussed along with its basic operating waveforms. Finally, the proof of concept for unsupervised learning in the array of a signal pattern provided at the network inputs is given.