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

The exploration of synaptic plasticity in metal-oxide-based ferroelectric thin-film transistors has been limited. As a perovskite ferroelectric material, LiNbO 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 LiNbO and LiAlO as a gate dielectric has been fabricated. This configuration reduces the depolarization field by leveraging the large ionic polarization of Li ions in the LiAlO layer, while the wide bandgap helps mitigate the leakage current. FeTFT exhibits impressive transistor performance, including a saturation mobility of 0.478 cmV s, an on/off ratio of 3.08 × 10, and a low trap-state density of 1.3 × 10 cm. 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.