REDUCING SUBSTRATE SURFACE SCRATCHING USING MACHINE LEARNING

Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training dat...

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Bibliographische Detailangaben
Hauptverfasser: Balaraman, Karthikeyan, Shah, Kartik B, Ramanathan, Karthik, Jackson, Michael Sterling, Hu, Weize, Xu, Wenjing, Radhakrishnan, Satish, Allen, Adolph Miller, Chong, Xinyuan, Sahu, Mitrabhanu, Chen, Feng
Format: Patent
Sprache:eng
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Zusammenfassung:Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training data and second training data are generated for the machine learning model. The first training data includes historical data associated with prior process recipe settings for a prior substrate temperature control process performed for a prior substrate at a prior process chamber. The second training data is associated with a historical scratch profile of one or more surfaces of the prior substrate after performance of the prior substrate temperature control process according to the prior process recipe settings. The first training data and the second training data are provided to train the machine learning model to predict which process recipe settings for the substrate temperature control process to be performed for the current substrate correspond to a target scratch profile for one or more surfaces of the current substrate.