Li memristor-based MOSFET synapse for linear I–V characteristic and processing analog input neuromorphic system

Abstract

In this research, we propose a method that can significantly improve the linearity of current–voltage characteristics (L–IV) of synapse devices. Considering that analog input data are dependent on the L–IV, synapse devices having non-linear current–voltage characteristics can result in drastic conductance variations during inference operations. It means that the L–IV is one of the key parameters in the synapse device. To improve the L–IV, a triode region of a metal oxide semiconductor field effect transistor (MOSFET) was utilized with a Li-ion-based memristor as a gate voltage divider, which results in gradual channel conductance changes (analog synaptic weights). The channel conductance of the MOSFET can be selectively controlled based on Li-ion intercalation and de-intercalation. A notably improved L–IV and analog synaptic weights were achieved, which enhanced the MNIST data set recognition accuracy from 35.8% to 92.03%.