Smart Diagnostics for 3D CFET: A Machine Learning Approach to Failure Analysis

This work introduces a novel Convolutional Neural Network for classifying transfer characteristics in emerging Gate-All-Around MOSFET. Trained on vast experimental dataset, the algorithm successfully identifies distinct failure modes across wafers with complex processing variations. The automated an...

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Bibliographic Details
Published inProceedings of the International Conference on Microelectronic Test Structures pp. 1 - 4
Main Authors Mitard, Jerome, Kocak, Husnu Murat, Chiarella, Thomas, Sheng, Cassie, Demuyck, Steven, Horiguchi, Naoto
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.03.2025
Subjects
Complementary FET
Failure analysis
Logic gates
Machine learning
Machine learning algorithms
Microelectronics
MOSFET
Next generation networking
Optimization
Three-dimensional displays
Yield estimation
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ISSN2158-1029
DOI10.1109/ICMTS63811.2025.11068926

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Summary:This work introduces a novel Convolutional Neural Network for classifying transfer characteristics in emerging Gate-All-Around MOSFET. Trained on vast experimental dataset, the algorithm successfully identifies distinct failure modes across wafers with complex processing variations. The automated analysis enables faster yield enhancement and process optimization for next-generation 3D MOSFET technologies.
ISSN:2158-1029
DOI:10.1109/ICMTS63811.2025.11068926