Artificial intelligence-based bias prediction for run-to-run process control

Abstract

Methods and systems for artificial intelligence (AI)-based bias prediction for run-to-run (R2R) process control are provided. A process recipe and a target outcome of a substrate process to be performed using one or more manufacturing equipment are identified. The process recipe and the target outcome are provided as an input to a machine learning model trained to predict biases of current substrate processes performed using manufacturing equipment in view of prior substrate processes performed using the manufacturing equipment. One or more outputs of the machine learning model are obtained, where the one or more outputs include a bias of the substrate process in view of one or more prior substrate processes performed using the manufacturing equipment. One or more settings of the process recipe are updated based on the bias.

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