Expediting Manufacturing Launch Using Integrated Data With AI/ML Analytic Solutions
Manufacturing yield, test time and quality are important metrics in new product introduction (NPI) to manufacturing safe launch. A powerful yield management system is crucial to achieve the goal metrics. In this paper, recommended yield management system selection criteria, data integration and machine learning (ML) methodologies along with innovative ways of using the selected yield management system to benefit safe manufacturing launch efficiency are introduced. Three examples of using a cloud yield tool including ML capabilities to expedite yield learning, test time reduction (TTR) and quality enhancement are presented.
Authors
- Jeffrey David
- Tomonori Honda (ORCID: https://orcid.org/0000-0003-2365-1378)
- R. Burch
- Helen Yu (ORCID: https://orcid.org/0000-0003-1445-7602)
- Scott D. Martin (ORCID: https://orcid.org/0000-0003-1263-5014)
- Laurenz van der Meer
- Wanting Xiong
- Edward Yang
- Sherry Lee
- Vaishnavi Reddipalli
Institutions
- PDF Solutions (United States) (US)
- Renesas Electronics (United States) (US)
Publication Details
- Journal
- IEEE Journal of the Electron Devices Society
- Published
- 2024-12-25
- DOI
- https://doi.org/10.1109/jeds.2024.3522126
- Citations
- 1
- Primary Topic
- Manufacturing Process and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.35