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

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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 4Mbit PCM test chips in 40 nm complementary metal oxide semiconductor (CMOS) process.

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Authors and Affiliations

  1. State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China

    Shuai Yan  (闫 帅), Daolin Cai  (蔡道林), Yifeng Chen  (陈一峰), Yuan Xue  (薛 媛), Yuanguang Liu  (刘源广), Lei Wu  (吴 磊) & Zhitang Song  (宋志棠)

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Shuai Yan  (闫 帅), Yuan Xue  (薛 媛), Yuanguang Liu  (刘源广) & Lei Wu  (吴 磊)

Authors
  1. Shuai Yan  (闫 帅)
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  2. Daolin Cai  (蔡道林)
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  3. Yifeng Chen  (陈一峰)
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  4. Yuan Xue  (薛 媛)
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  5. Yuanguang Liu  (刘源广)
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  6. Lei Wu  (吴 磊)
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  7. Zhitang Song  (宋志棠)
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Corresponding author

Correspondence to Daolin Cai  (蔡道林).

Additional information

Foundation item: the National Key Research and Development Program of China (Nos. 2017YFA0206101, 2017YFB0701703, 2017YFA0206104, 2017YFB0405601 and 2018YFB0407500), the National Natural Science Foundation of China (Nos. 61874178 and 61874129), the Project of the Science and Technology Council of Shanghai (No. 17DZ2291300), and the Shanghai Sailing Program (No. 19YF1456100)

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Yan, S., Cai, D., Chen, Y. et al. Reliability Modelling and Prediction Method for Phase Change Memory Using Optimal Pulse Conditions. J. Shanghai Jiaotong Univ. (Sci.) 25, 1–9 (2020). https://doi.org/10.1007/s12204-020-2153-8

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  • DOI: https://doi.org/10.1007/s12204-020-2153-8

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