Phase change memory (PCM) has reached the level of mass production. The first step in mass production is determining the proper pulse conditions of high-resistance (HR) and low-resistance (LR) states to realize the best performance of PCM chips on the basis of longer endurance characteristics. However, due to the neglect of each of the relations as well as the square term of each relationship for pulse conditions, the standard screening method for pulse conditions cannot accurately determine the optimal pulse conditions. A new statistical prediction method based on regression analysis is presented in this work. The method can model and predict the optimal pulse conditions of PCM chips on the basis of longer endurance characteristics. In the method, the parameter estimates, model equations and surface plot are generated by the least-mean-square (LMS) method for the regression analysis; the prediction model is established by monitoring the distributions of the resistance values collected from a 4 Kbit block of the 4 Mbit PCM test chips in 40 nm complementary metal oxide semiconductor (CMOS) process.
YAN Shuai (闫帅), CAI Daolin (蔡道林), CHEN Yifeng (陈一峰), XUE Yuan (薛媛), LIU Yuanguang (刘源广), WU Lei (吴磊), SONG Zhitang (宋志棠)
. Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions[J]. Journal of Shanghai Jiaotong University(Science), 2020
, 25(1)
: 1
-9
.
DOI: 10.1007/s12204-020-2153-8
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