An organic electrochemical synaptic transistor array for neuromorphic computation of sound localization
Yunchao Xu, Zhonghui Deng, Chenxing Jin, Wanrong Liu, Xiaofang Shi, Jianhui Chang, Haoran Yu, Biao Liu, Jia Sun, Junliang Yang
Abstract
Neuromorphic devices have a potential to accelerate high-performance parallel and low-power memory computing, artificial intelligence, and adaptive learning. In this work, a facile and high-resolution patterning process is introduced to fabricate an organic electrochemical synaptic transistors (OESTs) array using a laser etching process and screen-printing ion gel. The OESTs show an excellent electrical-pulse-modulated conductance updating for synaptic functions and also remarkable mechanical flexibility and low energy consumption. Based on the linear, repeatable, and stable long-term plasticity, the long-term potentiation statistics of 2205 count points have been simulated to explore the regularity of their conductivity states. Furthermore, the sound-localization function was simulated by constructing a cross-grid array of OESTs. The normalized mean square error of sound localization results was reduced by ∼37.5% from the untrained period. This work provides a platform for designing a high-performance, flexible, and highly efficient neuromorphic computation for artificial neuromorphic systems.