Guo Zhenqiang, Liu Gongjie, Sun Yong, Zhang Yinxing, Zhao Jianhui, Liu Pan, Wang Hong, Zhou Zhenyu, Zhao Zhen, Jia Xiaotong, Sun Jiameng, Shao Yiduo, Han Xu, Zhang Zixuan, Yan Xiaobing
Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, P. R. China.
ACS Nano. 2023 Nov 14;17(21):21518-21530. doi: 10.1021/acsnano.3c06510. Epub 2023 Oct 28.
Neuromorphic computing based on memristors capable of in-memory computing is promising to break the energy and efficiency bottleneck of well-known von Neumann architectures. However, unstable and nonlinear conductance updates compromise the recognition accuracy and block the integration of neural network hardware. To this end, we present a highly stable memristor with self-assembled vertically aligned nanocomposite (VAN) SrTiO:MgO films that achieve excellent resistive switching with low set/reset voltage variability (4.7%/-5.6%) and highly linear conductivity variation (nonlinearity = 0.34) by spatially limiting the conductive channels at the vertical interfaces. Various synaptic behaviors are simulated by continuously modulating the conductance. Especially, convolutional image processing using diverse crossbar kernels is demonstrated, and the artificial neural network achieves an overwhelming recognition accuracy of up to 97.50% for handwritten digits. Even under the perturbation of Poisson noise (λ = 10), 6% Salt and Pepper noise, and 5% Gaussian noise, the high recognition accuracies are retained at 95.43%, 94.56%, and 95.97%, respectively. Importantly, the logic memory function is proven experimentally based on the nonvolatile properties. This work provides a material system and design idea to achieve high-performance neuromorphic computing and logic operation.
基于能够进行内存计算的忆阻器的神经形态计算有望突破著名的冯·诺依曼架构在能量和效率方面的瓶颈。然而，不稳定和非线性的电导更新会影响识别精度，并阻碍神经网络硬件的集成。为此，我们展示了一种具有自组装垂直排列纳米复合材料（VAN）SrTiO:MgO薄膜的高度稳定忆阻器，通过在垂直界面处对导电通道进行空间限制，实现了出色的电阻开关，具有低的设置/重置电压变化率（4.7%/-5.6%）和高度线性的电导率变化（非线性度 = 0.34）。通过连续调制电导来模拟各种突触行为。特别是，展示了使用不同交叉开关内核的卷积图像处理，并且人工神经网络对手写数字的识别准确率高达97.50%。即使在泊松噪声（λ = 10）、6%的椒盐噪声和5%的高斯噪声的干扰下，高识别准确率仍分别保持在95.43%、94.56%和95.97%。重要的是，基于非易失性特性通过实验证明了逻辑存储功能。这项工作提供了一种实现高性能神经形态计算和逻辑运算的材料系统和设计理念。
