Ferroelectric Artificial Synapses for Recognition of a Multishaded Image

Abstract

We demonstrate, for the first time, the on-chip pattern recognition of a multishaded grayscale image in a neural network circuit with multiple neurons. This pattern recognition is based on a spiking neural network model that uses multiple three-terminal ferroelectric memristors (3T-FeMEMs) as synapses. The synapse chip of the neural network is formed by stacking CMOS circuits and 3T-FeMEMs. The conductance of the 3T-FeMEM is gradually changed in the linear range by varying the amplitude of the applied voltage pulse. Using the analog and nonvolatile conductance change of the 3T-FeMEM as synaptic weight, the matrix patterns are learned after the spike timing-dependent plasticity learning rule. Even when an incomplete multishaded pattern is input to the neural network circuit, it automatically completes and recalls a previously learned pattern.