Reducing substrate surface scratching using machine learning

US11586160B2 · kind B2 · utility

Assignee

• Applied Materials, Inc. · US

Inventors

• Kartik Shah · Saratoga, US
• Satish Radhakrishnan · San Jose, US
• Karthik Ramanathan · Survanagiri, IN
• Karthikeyan Balaraman · Kanchinakote, IN
• Adolph Miller Allen · Oakland, US
• Xinyuan Chong · Santa Clara, US
• Mitrabhanu Sahu · San Jose, US
• Wenjing Xu · San Jose, US
• Michael S. Jackson · Sunnyvale, US
• Weize Hu · Sunnyvale, US
• Feng Chen · San Jose, US

Key dates

• Filing date: Jun 28, 2021
• Grant date: Feb 21, 2023
• Expiry date: Jun 28, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G05B2219/45031
• WIPO field: Semiconductors
• WIPO sector: Electrical engineering

Abstract

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.