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 analysis enables faster yield enhancement and process optimization for next-generation 3D MOSFET technologies.
Authors
- Hüsnü Murat Koçak (ORCID: https://orcid.org/0000-0001-7766-8154)
- Naoto Horiguchi (ORCID: https://orcid.org/0000-0001-5490-0416)
- T. Chiarella
- Cassie Sheng
- Jérôme Mitard (ORCID: https://orcid.org/0000-0002-7422-079X)
- Steven Demuyck
Institutions
- Imec the Netherlands (NL)
- IMEC (BE)
Publication Details
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1109/icmts63811.2025.11068926
- Citations
- 1
- Primary Topic
- Integrated Circuits and Semiconductor Failure Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- HORIZON EUROPE Framework Programme