CMP Process Optimization Engineering by Machine Learning

Advanced Chemical-mechanical polishing (CMP) process not only needs to maintain stable run-to-run thickness control but also achieve better within wafer/within chip planarization performance. Furthermore, slurries or other consumable parts, like PAD and Disks selection are also the keys for CMP proc...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing 2021-08, Vol.34 (3), p.280-285
Main Authors: Yu, Hsiang-Meng, Lin, Chih-Chen, Hsu, Min-Hsuan, Chen, Yen-Ting, Chen, Kuang-Wei, Luoh, Tuung, Yang, Ling-Wuu, Yang, Tahone, Chen, Kuang-Chao
Format: Article
Language:English
Subjects:
Algorithms
artificial intelligence
Artificial neural networks
Chemical-mechanical polishing
closed loop systems
Disks
Layout
Layouts
Machine learning
Neural networks
Optimization
Prediction algorithms
Predictive models
Process control
Semiconductor device modeling
Semiconductor process modeling
Slurries
Test vehicles
thickness control
Topology
Wafers
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Summary:Advanced Chemical-mechanical polishing (CMP) process not only needs to maintain stable run-to-run thickness control but also achieve better within wafer/within chip planarization performance. Furthermore, slurries or other consumable parts, like PAD and Disks selection are also the keys for CMP process optimization. The most difficult thing in CMP process is to have capability to predict and cover the various topologies and layout densities patterned wafers and preventing the hot spots occurrences. In this study, different Neural-Network algorithm with data pre-processing models are implemented to the in-line CMP CLC tuning and dishing/erosion prediction at various topology/pattern density test vehicle pattern wafers. Transfer learning technique is implemented on the original Neural -Network algorithm model, the behavior of real product can be simulated and predicted based on the knowledge of test vehicle database successfully. With the aid of multiple layer erosion/ dishing Neural-Network algorithm model prediction, the potential high risky hot spots can be highlighted at the initial layout design stage, then further shorten the turn-around time of design layout validation.
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2021.3072361