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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
CE 6309 Georgios Makris | |||||
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
CE 6309 Georgios Makris | |||||
Georgios Makris
[email protected]Grades: 263
Median GPA: B+
Mean GPA: 3.296
3.1
Professor rating
3.5
Difficulty
8
Ratings given
60%
Would take again
Applications of Machine Learning in Semiconductor IC Manufacturing and Test
CE 6309
Erik Jonsson School of Engineering and Computer Science
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. 3 credit hours.
Prerequisite: CE 6301 or EEDG 6301 or CE 6303 or EEDG 6303 or CE 6325 or EECT 6325 or EECT 6326.
Offering Frequency: Each year
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
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Georgios Makris
[email protected]Grades: 263
Median GPA: B+
Mean GPA: 3.296
3.1
Professor rating
3.5
Difficulty
8
Ratings given
60%
Would take again
Applications of Machine Learning in Semiconductor IC Manufacturing and Test
CE 6309
Erik Jonsson School of Engineering and Computer Science
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. 3 credit hours.
Prerequisite: CE 6301 or EEDG 6301 or CE 6303 or EEDG 6303 or CE 6325 or EECT 6325 or EECT 6326.
Offering Frequency: Each year
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
Click a checkbox to add something to compare.