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    Flexible Floating-Gate Electric-Double-Layer Organic Transistor for Neuromorphic Computing

    Year: 2022

    Journal: ACS Appl. Mater. Interfaces, Volume 14, DEC 28, page 57102–57112

    Authors: Zheng, Chaoyue; Liao, Yuan; Wang, Junjie; Zhou, Ye; Han, Su-Ting

    Organizations: NSFC Program [62122055, 62074104, 62104154, 61974093, 62001307]; Guangdong Provincial Department of Science and Technology [2018B030306028, 2021A1515012569]; Science and Technology Innovation Commission of Shenzhen [20200804172625001, 20210324095207020]

    Keywords: flexible floating-gate electric-double-layer transistor; interfacial modification; weight update; nonlinearity; flexible computing

    The key to the study of flexible neuromorphic computing is the excellent weight update characteristic of neuromorphic devices. Electric-double-layer transistors (EDLTs) include high transconductance, excellent stability of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent switching properties. However, up to now, there is no report on a flexible EDLT that provides all the aforementioned performance characteristics. Here, a planar flexible floating-gate EDLT including an excellent linear/symmetric weight update, a large number (>800) of conductance states, repetitive switching endurance (>100 cycles), and low variation in weight update is reported. After 800 signal stimulations, it is found that the nonlinearity values of LTP are between 0.20 and 0.85, those of LTD fall between 0.66 and 1.55, the symmetricity values are between 120.7 and 639.8, and the dynamic range is between 150 and 352 nS. The study of 8 x 8 flexible floating-gate EDLT arrays shows that the average deviation and standard deviation between the experimental and theoretical values are 1.36 and 1.93, respectively, indicating that the conductance regulation in the array has a relatively small deviation. The different bending angles and the mechanical stability of the floating-gate EDLT are also studied, which exhibit the excellent bending properties. Furthermore, we studied the recognition of MNIST handwritten digit images by a three-layer perceptron artificial neural network with the experimental weight update, and the maximal recognition accuracy is up to 87.8%.
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