Electrolyte-gated transistors with good retention for neuromorphic computing

Abstract

Electrolyte-gated transistors (EGTs) provide prominent analog switching performance for neuromorphic computing. However, suffering from self-discharging nature, the retention performance greatly hampers their practical applications. In this Letter, we realize a significant improvement in EGT retention by inserting a SiO2 layer between the gate electrode and electrolyte. The dynamic process behind the improvement is interpreted by an assumptive leakage-assisted electrochemical mechanism. In addition to improved retention, analog switching with a large dynamic range, superior linearity and symmetry, and low variation has been achieved using identical voltage pulses. Based on the experimental data, a nearly ideal recognition accuracy of 98% has been demonstrated by simulations using the handwritten digit data sets. The obtained results pave a way for employing EGT in future neuromorphic computing.