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Conference Papers Year : 2023

Random Forest Based R2R Control: Application to Chemical Mechanical Planarization Process

Abstract

In this paper, a Random Forest based run-to-run controller is developed to meet the challenges of a high-mix production. The model that relates the optimal recipe parameter with various industrial features is trained off-line from historical data. The predicted optimal recipe parameter is used on-line to tune the process condition. Numerical experiments conducted on a Chemical Mechanical Planarization process are presented to illustrate the performance of the controller. Compared with the system in production, the proposed approach demonstrates higher flexibility and efficiency.
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Dates and versions

emse-04184123 , version 1 (21-08-2023)

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Lucile Terras, Cyril Alegret, François Pasqualini, Agnes Roussy. Random Forest Based R2R Control: Application to Chemical Mechanical Planarization Process. 2023 34th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), May 2023, Saratoga Springs, United States. pp.1-6, ⟨10.1109/ASMC57536.2023.10121144⟩. ⟨emse-04184123⟩
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