第一作者：Xin Li
通讯作者：Qiang He,Hao Tong
合写作者：Xiangshui Miao
发表刊物：IEEE Electron Device Letters
收录刊物：SCI
学科门类：工学
一级学科：电子科学与技术
文献类型：J
关键字：Resistance,Programming,Crystallization,Threshold voltage,Switches,Phase change memory,Phase change materials
摘要：Phase change materials, which has been focused on the non-volatile memory field, show the possibility to carry out data storage and computing in the same physical location. However, the resistance drift behavior of phase change memory has been a huge barrier not only to traditional binary memory application for a long time, but to multi-level storage and therefore the neural network computing. Here, a bipolar programming scheme is exploited to achieve drift-reduced intermediate states and convolutional neural network (CNN) computations in Ge2Sb2Te5 (GST) based memory cells. Experiments show that the resistance drift phenomena under bipolar programming have been reduced. Furthermore, the impact of bipolar operation on CNN for inference is investigated. This work provides effective means for implementing phase change neuromorphic processor with enhanced stability.