Applied Materials, Inc.

Weize Hu

Adolph Miller Allen

Xinyuan Chong

Kartik Bhupendra Shah

Feng Chen

Michael Sterling Jackson

Karthik Ramanathan

Satish Radhakrishnan

Wenjing Xu

Karthikeyan Balaraman

Mitrabhanu Sahu

Philip Allan Kraus

Kelvin Chan

Kenric T Choi

Amir Bayati

William John Durand

Michelle SanPedro

Abdullah Zafar

Sarah Langlois White

Elaina Noelle Babayan

Methods and apparatus for processing a substrate are provided herein. For example, a gas supply configured for use with a processing chamber includes an ampoule that stores a precursor and comprises an input to receive a carrier gas and an output to provide a mixture of the carrier gas and the precursor to the processing chamber and a sensor assembly comprising a detector and an infrared source operably connected to an outside of an enclosure, through which the mixture flows, and a gas measurement volume disposed within the enclosure and along an inner wall thereof so that a concentration of the precursor in the mixture can be measured by the detector and transmitted to a controller.

Methods and apparatus for processing a substrate

Process recipe data associated a process to be performed for a substrate at a process chamber is provided as input to a trained machine learning model. A set of process recipe settings for the process that minimizes scratching on one or more surfaces of the substrate is determined based on one or more outputs of the machine learning model. The process is performed for the substrate at the process chamber in accordance with the determined set of process recipe settings.

Reducing substrate surface scratching using machine learning

Methods and apparatus for controlling precursor flow are provided. In embodiments, the methods and apparatus apparatus for controlling precursor flow to a deposition chamber, includes: an ampoule to output a precursor; a sensor assembly communicatively coupled to the ampoule; and a control system, wherein the control system is configured to calibrate the sensor assembly during flow of a precursor or a chemical standard through the sensor assembly.

Methods and apparatus for calibrating concentration sensors for precursor delivery

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.

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Sunnyvale, CA, United States of America

Weize Hu