Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou313001, P.R. China.
College of Electronics and Information Engineering, Shenzhen University, Shenzhen518060, P.R. China.
ACS Appl Mater Interfaces. 2022 Dec 28;14(51):57102-57112. doi: 10.1021/acsami.2c20925. Epub 2022 Dec 14.
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 × 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%.
柔性神经形态计算研究的关键在于神经形态器件出色的权重更新特性。 电双层晶体管（EDLT）具有高跨导、阈值电压稳定性好、线性权重更新和重复的离子浓度依赖性开关特性。 然而，到目前为止，还没有关于提供所有上述性能特性的柔性 EDLT 的报道。 在这里，我们报道了一种包括出色的线性/对称权重更新、大量 (>800) 电导状态、重复开关耐久性 (>100 个周期) 和权重更新变化小的平面柔性浮栅 EDLT。 在 800 次信号刺激后，发现 LTP 的非线性值在 0.20 到 0.85 之间，LTD 的非线性值在 0.66 到 1.55 之间，对称性值在 120.7 到 639.8 之间，动态范围在 150 到 352 nS 之间。 对 8×8 个柔性浮栅 EDLT 阵列的研究表明，实验值与理论值之间的平均偏差和标准偏差分别为 1.36 和 1.93，表明阵列中的电导调节具有较小的偏差。 还研究了不同弯曲角度和浮栅 EDLT 的机械稳定性，结果表明其具有出色的弯曲性能。 此外，我们通过三层感知器人工神经网络研究了实验权重更新对手写数字图像 MNIST 的识别，最大识别准确率高达 87.8%。
