Memory stability analysis traditionally relied heavily on circuit simulation-based approaches that run Monte Carlo (MC) analysis over various manufacturing and use condition parameters. This paper researches application of Machine Learning approaches for memory element failure analysis which could mimic simulation-like accuracy and minimize the need for engineers to rely heavily on simulators for their validations. Both regressor and classifier algorithms are benchmarked for accuracy and recall scores. A high recall score implies fewer escapes of fails to field and is the metric of choice for comparing algorithm. The paper identifies that recall score in excess of 0.97 can be achieved through stack ensemble and logistic regression-based approaches. The high recall score suggests machine learning based approaches can be used for memory failure rate assessments.
Thanniru, Ravindra; Kapila, Gautam; and Lohia, Nibhrat
"Machine Learning Approach to Stability Analysis of Semiconductor Memory Element,"
SMU Data Science Review: Vol. 5
, Article 11.
Available at: https://scholar.smu.edu/datasciencereview/vol5/iss3/11
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