High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO<sub>3</sub>:MgO Film Memristor
Zhenqiang Guo, Gongjie Liu, Yong Sun, Yinxing Zhang, Jianhui Zhao, Pan Liu, Hong Wang, Zhenyu Zhou, Zhen Zhao, Xiaotong Jia, Jiameng Sun, Yiduo Shao, Xu Han, Zixuan Zhang, Xiaobing Yan
Abstract
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 3: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.