Zhang Hengjie, Jiang Biyi, Cheng Chuantong, Huang Beiju, Zhang Huan, Chen Run, Xu Jiayi, Huang Yulong, Chen Hongda, Pei Weihua, Chai Yang, Zhou Feichi
School of Microelectronics, Southern University of Science and Technology, Shenzhen 518000, People's Republic of China.
The State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China.
Nano Lett. 2023 Apr 26;23(8):3107-3115. doi: 10.1021/acs.nanolett.2c03624. Epub 2023 Apr 12.
Two-terminal self-rectifying (SR)-synaptic memristors are preeminent candidates for high-density and efficient neuromorphic computing, especially for future three-dimensional integrated systems, which can self-suppress the sneak path current in crossbar arrays. However, SR-synaptic memristors face the critical challenges of nonlinear weight potentiation and steep depression, hindering their application in conventional artificial neural networks (ANNs). Here, a SR-synaptic memristor (Pt/NiO/WO:Ti/W) and cross-point array with sneak path current suppression features and ultrahigh-weight potentiation linearity up to 0.9997 are introduced. The image contrast enhancement and background filtering are demonstrated on the basis of the device array. Moreover, an unsupervised self-organizing map (SOM) neural network is first developed for orientation recognition with high recognition accuracy (0.98) and training efficiency and high resilience toward both noises and steep synaptic depression. These results solve the challenges of SR memristors in the conventional ANN, extending the possibilities of large-scale oxide SR-synaptic arrays for high-density, efficient, and accurate neuromorphic computing.
两端自整流（SR）突触忆阻器是高密度和高效神经形态计算的卓越候选者，特别是对于未来的三维集成系统，其能够在交叉阵列中自抑制潜行路径电流。然而，SR突触忆阻器面临非线性权重增强和陡峭抑制的关键挑战，这阻碍了它们在传统人工神经网络（ANN）中的应用。在此，介绍了一种具有潜行路径电流抑制特性和高达0.9997的超高权重增强线性度的SR突触忆阻器（Pt/NiO/WO:Ti/W）和交叉点阵列。基于该器件阵列展示了图像对比度增强和背景滤波。此外，首次开发了一种无监督自组织映射（SOM）神经网络用于方向识别，具有高识别准确率（0.98）、训练效率以及对噪声和陡峭突触抑制的高弹性。这些结果解决了传统ANN中SR忆阻器的挑战，扩展了大规模氧化物SR突触阵列用于高密度、高效和精确神经形态计算的可能性。
