Applied Materials, Inc.

Chao-Hsien Lee

Pengyu Han

Jui-Che Lin

Shauh-Teh Juang

Todd J Egan

Lei Lian

Barry Craver

Zhengping Yao

Yan-Jhu Chen

Wallace Wang

Pin Ham Lu

Wan Hsueh Lai

Shu Yu Chen

An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to generate a characteristic sequence defining a relationship between at least two manufacturing parameters, and determine a relationship between one or more variables related to the feature and the characteristic sequence. The manufacturing system is further configured to determine a weight based on the determined relationship and apply the weight to the feature. The manufacturing system is further configured to train a machine-learning model in view of the weighted feature.

Methods and mechanisms for preventing fluctuation in machine-learning model performance

An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to train a machine-learning model based on the input data reflecting the feature. The manufacturing system is further configured to modify the machine-learning model in view of the virtual knob for the feature.

Methods and mechanisms for generating virtual knobs for model performance tuning

Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model. In response to a determination that the respective metrology measurement satisfies a metrology measurement criterion, an instruction including a command to terminate the current process is generated.

Substrate process endpoint detection using machine learning

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Taoyuan, Taiwan

Chao-Hsien Lee