Name
Abstract | ||
|---|---|---|
In the competitive semiconductor manufacturing industry where large amounts of data are generated, data driven quality control technologies are gaining increasing importance. In this work, we build machine learning models for high yield and time varying semiconductor manufacturing processes. Challenges include class imbalance and concept drift. Batch, online and incremental learning frameworks are developed to overcome these challenges. We study the packaging and testing process in chip stack flash memory as an application, and show the possibility of yield improvement with machine learning based classifiers detecting bad dies before packaging. Experimental results demonstrate significant yield improvement potential using real data from industry. Without concept drift, for stacks of 8 dies, an approximately 9% yield improvement can be achieved. In a longer period of time with realistic concept drift, our incremental learning approach achieves approximately 1.4% yield improvement in the 8 die stack case and 3.4% in the 16 die stack case.
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Year | Venue | Field |
|---|---|---|
2017 | ICCAD | Flash memory,Data-driven,Stack (abstract data type),Computer science,Circuit reliability,Semiconductor device fabrication,Chip,Concept drift,Die (manufacturing),Artificial intelligence,Machine learning |
DocType | ISSN | ISBN |
Conference | 1933-7760 | 978-1-4503-5950-4 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongge Chen | 1 | 36 | 4.58 |
| Duane Boning | 2 | 201 | 49.37 |