MACHINE LEARNING MODEL FOR SEMICONDUCTOR MANUFACTURING PROCESSES

The disclosure describes methods and systems for training and deploying a machine learning predictive model for use in a semiconductor manufacturing process. Specifically, the present disclosure provides for training machine learning predictive models for manufacturing components using design data,...

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Bibliographische Detailangaben
Hauptverfasser:	LOH, Joanna Kejun, HUANG, Zhiqiang, LINDLEY, Roger Alan, TAN, Li Ming, KOENTJORO, Olivia Fatma
Format:	Patent
Sprache:	eng
Schlagworte:	BASIC ELECTRIC ELEMENTS CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR ELECTRICITY FUNCTIONAL ELEMENTS OF SUCH SYSTEMS MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PHYSICS REGULATING SEMICONDUCTOR DEVICES
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Beschreibung
Zusammenfassung:	The disclosure describes methods and systems for training and deploying a machine learning predictive model for use in a semiconductor manufacturing process. Specifically, the present disclosure provides for training machine learning predictive models for manufacturing components using design data, process parameters, gas flow configurations from a pixelated showerhead, temperature profile across an electrostatic chuck, and measured uniformity profiles of processed wafers. The present disclosure also provides for deploying the machine learning predictive model to effectuate real-time adjustments to a manufacturing process.