Li Yesheng, Chen Shuai, Yu Zhigen, Li Sifan, Xiong Yao, Pam Mer-Er, Zhang Yong-Wei, Ang Kah-Wee
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.
Department of Microstructure, School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
Adv Mater. 2022 Jul;34(26):e2201488. doi: 10.1002/adma.202201488. Epub 2022 May 6.
In-memory computing based on memristor arrays holds promise to address the speed and energy issues of the classical von Neumann computing system. However, the stochasticity of ions' transport in conventional oxide-based memristors imposes severe intrinsic variability, which compromises learning accuracy and hinders the implementation of neural network hardware accelerators. Here, these challenges are addressed using a low-voltage memristor array based on an ultrathin PdSeO /PdSe heterostructure switching medium realized by a controllable ultraviolet (UV)-ozone treatment. A distinctively different ions' transport mechanism is revealed in the heterostructure that can confine the formation of conductive filaments, leading to a remarkable uniform switching with low set and reset voltage variability values of 4.8% and -3.6%, respectively. Moreover, convolutional image processing is further implemented using various crossbar kernels that achieve a high recognition accuracy of ≈93.4% due to the highly linear and symmetric analog weight update as well as multiple conductance states, manifesting its potential beyond von Neumann computing.
基于忆阻器阵列的内存计算有望解决传统冯·诺依曼计算系统的速度和能耗问题。然而，传统氧化物基忆阻器中离子传输的随机性带来了严重的固有变化性，这会降低学习精度并阻碍神经网络硬件加速器的实现。在此，通过基于超薄PdSeO /PdSe异质结构开关介质的低压忆阻器阵列解决了这些挑战，该介质通过可控的紫外线（UV）-臭氧处理实现。在异质结构中揭示了一种截然不同的离子传输机制，该机制可以限制导电细丝的形成，从而实现显著的均匀开关，其设置和重置电压变化率分别低至4.8%和-3.6%。此外，使用各种交叉开关内核进一步实现了卷积图像处理，由于高度线性和对称的模拟权重更新以及多个电导状态，实现了约93.4%的高识别准确率，彰显了其超越冯·诺依曼计算的潜力。
