Emulating Low-Power Synaptic Plasticity in a Solution-Processed Oxide-Based Long Retention Memory Transistor with High Learning Accuracy.

Abstract

The exploration of synaptic plasticity in metal-oxide-based ferroelectric thin-film transistors has been limited. As a perovskite ferroelectric material, LiNbO3 is widely studied; but its potential use as a neuromorphic device, like synaptic transistors, has not been realized. In this study, a solution-processed ferroelectric thin-film transistor (FeTFT) with an alternating layer of LiNbO3 and Li5AlO4 as a gate dielectric has been fabricated. This configuration reduces the depolarization field by leveraging the large ionic polarization of Li+ ions in the Li5AlO4 layer, while the wide bandgap helps mitigate the leakage current. FeTFT exhibits impressive transistor performance, including a saturation mobility of 0.478 cm2V-1 s-1, an on/off ratio of 3.08 × 103, and a low trap-state density of 1.3 × 1013 cm-2. Moreover, the device demonstrates good memory retention, retaining information for nearly 1 day. It successfully emulates synaptic plasticity, specifically short-term plasticity and long-term plasticity. Besides, a 94% training accuracy has been achieved through artificial neural network simulation. Notably, the FeTFT consumes minimal power, with energy consumption of approximately 3.09 nJ per synaptic event, which is remarkably low compared to other reported solution-processed FeTFT devices.