神经形态工程学
材料科学
计算机科学
晶体管
光电子学
突触重量
电子工程
人工神经网络
人工智能
电气工程
工程类
电压
作者
Yuanfan Xu,Zhonghui Deng,Chenxing Jin,Wanrong Liu,Xiaofang Shi,Jianhui Chang,Haiyang Yu,Biao Liu,Jia Sun,Junliang Yang
摘要
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.
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