Relaxation Signal Analysis and Optimization of Analog Resistive Random Access Memory for Neuromorphic Computing
Siyao Yang, Qi Hu, Bin Gao, Jianshi Tang, Feng Xu, Yuyao Lu, Peng Yao, Yue Xi, He Qian, Huaqiang Wu
Abstract
The relaxation effect in analog resistive random access memory (RRAM) poses a significant challenge in the implementation of neuromorphic systems, as it leads to a loss of accuracy in computing. However, due to the inherent interdependence of various fluctuations and underlying mechanisms, the relaxation effect is still challenging. In this study, we have developed a high-quality adaptive relaxation signal analysis method by analyzing the read current during the relaxation process. This method enables the identification of all conductivity demarcation points in relaxation and categorizes them into different types of fluctuations. Importantly, we have investigated distinct fluctuations in relaxation and their corresponding mechanisms, which is a comprehensive analysis of fluctuations in the relaxation effect. We propose an optimization strategy based on our understanding of these mechanisms: increasing the pulsewidth. This strategy aims to mitigate relaxation effects and reduce the relative accuracy loss of convolutional neural networks (CNNs).