CE6309 - Applications of Machine Learning in Semiconductor IC Manufacturing and Test

CE 6309 (EEDG 6309) Applications of Machine Learning in Semiconductor IC Manufacturing and Test (3 semester credit hours) Fundamentals of machine learning, including regression, classification, feature extraction, feature selection, synthetic training set enhancement, boosting and curse of dimensionality; test cost reduction via test compaction, alternate test, adaptive test and effectiveness metrics; wafer-level spatial and spatio-temporal correlation modeling, process variation decomposition, process monitoring, outlier detection yield prediction; post-manufacturing tuning and post-deployment calibration of analog/RF ICs; security and trust assessment, including hardware Trojan detection, counterfeit IC identification, and fab-of-origin attestation. Experience with Machine Learning methods and software desirable but not required. Prerequisite: CE 6301 or EEDG 6301 or CE 6303 or EEDG 6303 or CE 6325 or EECT 6325 or EECT 6326. (3-0) Y