Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions

doi:10.1007/s12204-020-2153-8

Journal of Shanghai Jiao Tong University (Science) ›› 2020, Vol. 25 ›› Issue (1): 1-9.doi: 10.1007/s12204-020-2153-8

Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions

YAN Shuai ^1,2 (闫帅), CAI Daolin ^1* (蔡道林), CHEN Yifeng ¹ (陈一峰), XUE Yuan ^1,2 (薛媛), LIU Yuanguang ^1,2 (刘源广), WU Lei ^1,2 (吴磊), SONG Zhitang¹ (宋志棠)

(1. State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China)

出版日期:2020-01-15 发布日期:2020-01-12
通讯作者: CAI Daolin (蔡道林) E-mail:caidl@mail.sim.ac.cn

Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions

YAN Shuai ^1,2 (闫帅), CAI Daolin ^1* (蔡道林), CHEN Yifeng¹(陈一峰), XUE Yuan^1,2 (薛媛), LIU Yuanguang ^1,2 (刘源广), WU Lei ^1,2 (吴磊), SONG Zhitang¹ (宋志棠)

(1. State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China)

Online:2020-01-15 Published:2020-01-12
Contact: CAI Daolin (蔡道林) E-mail:caidl@mail.sim.ac.cn

摘要/Abstract

摘要： 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.

关键词: phase change memory (PCM), prediction model, regression analysis, pulse conditions

Abstract: 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.

Key words: phase change memory (PCM), prediction model, regression analysis, pulse conditions

中图分类号:

TN 3
O 213

YAN Shuai (闫帅), CAI Daolin (蔡道林), CHEN Yifeng (陈一峰), XUE Yuan (薛媛), LIU. Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(1): 1-9.

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 Jiao Tong University (Science), 2020, 25(1): 1-9.

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Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions

Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions

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