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用于节能神经形态计算的柔性有机电化学晶体管

Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing.

作者信息

Zhu Li, Lin Junchen, Zhu Yixin, Wu Jie, Wan Xiang, Sun Huabin, Yu Zhihao, Xu Yong, Tan Cheeleong

机构信息

College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Yongjiang Laboratory (Y-LAB), Ningbo 315202, China.

出版信息

Nanomaterials (Basel). 2024 Jul 12;14(14):1195. doi: 10.3390/nano14141195.

Abstract

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.

摘要

受大脑启发的柔性神经形态器件对于下一代高效可穿戴传感和计算系统具有重要意义。在本文中,我们提出了一种柔性有机电化学晶体管,它使用聚[(联噻吩)-交替-(2,5-二(2-辛基十二烷基)-3,6-二(噻吩基)-吡咯并吡咯烷酮)](DPPT-TT)作为有机半导体,聚甲基丙烯酸甲酯(PMMA)/LiClO固态电解质作为栅极介电层。在栅极电压调制下,介电层和沟道之间形成双电层(EDL),使器件能够在低电压下工作。此外,通过利用器件内部的双层效应和电化学掺杂,我们成功模拟了各种突触行为,包括兴奋性突触后电流(EPSC)、双脉冲易化(PPF)、高通滤波特性、从短期可塑性(STP)到长期可塑性(LTP)的转变,并在3×3阵列中展示了其图像识别和存储能力。重要的是,即使在弯曲后,器件的电学性能仍然稳定,在-0.001 V时每个突触事件实现了2.08 fJ的超低功耗。这项研究可能有助于超低功耗神经形态计算、仿生机器人和人工智能的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e970/11279808/91c0a35c1375/nanomaterials-14-01195-g001.jpg

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