MACHINE AND DEEP LEARNING TECHNIQUES FOR PREDICTING ECOLOGICAL EFFICIENCY IN SUBSTRATE PROCESSING

In some embodiments, a method includes receiving a process recipe including process recipe setpoint data. The method further includes inputting the process recipe into one or more trained machine learning models that output predicted environmental resource usage data indicative of an environmental r...

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Bibliographische Detailangaben
Hauptverfasser:	Murayama, Satomi, Neville, Elizabeth, Moradian, Ala, Kelkar, Umesh Madhav, Trejo, Orlando
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COUNTING FUNCTIONAL ELEMENTS OF SUCH SYSTEMS MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PHYSICS REGULATING
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Beschreibung
Zusammenfassung:	In some embodiments, a method includes receiving a process recipe including process recipe setpoint data. The method further includes inputting the process recipe into one or more trained machine learning models that output predicted environmental resource usage data indicative of an environmental resource consumption associated with processing a substrate in a process chamber according to the process recipe. The method further includes outputting a recommendation associated with the process recipe based at least in part on the predicted environmental resource usage data.