Investigating Series and Parallel Oxide Memtransistors for Tunable Weight Update Properties

Abstract

Currently,analog in-memory computing, employing memristorsintoa crossbar array architecture (CAA), is the leading system among availableneuromorphic hardware. This study presents a highly tunable synapticweight update based on a multiterminal memtransistor device as a solutionfor nonlinear synaptic operations and crosstalk issues in CAA memristors,which are long-standing challenges in neuromorphic hardware applications.To explore an effective device structure for tunable weight updateproperties, a memtransistor device with a series and parallel structurefunctioning by interface type and oxygen migration is fabricated usinga ZnO channel layer and an amorphous TiO2 memristor. Theseries memtransistor device exhibits a significant tunable weightupdate property at the gate knob; thus, it simultaneously can functionas a selector (accelerating and inhibiting weight update) in the CAAand tune and ultimately improve the linearity of the potentiationand depression curves. Neuromorphic hardware based on tunable synapticweight update functions provides advantageous features for accuracyand crosstalk issues. Using the Fashion-MNIST pattern recognitionsimulation, the tuned weight update properties are obtained by threedifferent write and read condition combinations, and the results areclose to ideal accuracy.