Modeling and Optimizing the Impact of Process and Equipment Parameters in Sputtering Deposition Systems Using a Gaussian Process Machine Learning Framework

We present a method for empirically modeling and optimizing variations in sputtering deposition processes using Gaussian Process (GP) machine learning methods. Our predictive models can be trained with limited training data to enable rapid sputtering process tuning. As a first case, we model the effect of process recipe parameters such as chamber pressure and power on sputtered film thickness uniformity. A second more challenging case is also demonstrated: modeling film thickness spatial uniformity as a function of equipment configuration parameters. The effects of the chamber configuration variables are complex, motivating incorporation of prior process knowledge into the GP framework by utilizing a physics-based solver. Because adjusting equipment configuration parameters and obtaining corresponding wafer fabrication data is costly, a key metric is the expected number of tunes required until process constraints are met. Using past experimental data, we show that tunes using the GP-based predictive model are expected to converge in significantly fewer iterations compared to tunes using polynomial, gradient boosted regression tree, multivariate spline, and deep learning based modeling methods.

Automated summary

We propose a method for empirically modeling and optimizing variations in sputtering deposition processes using Gaussian Process (GP) machine learning methods. Our models can be trained with limited data to enable fast tuning of sputtering processes. We demonstrate two cases: modeling the effect of process recipe parameters and equipment configuration parameters on sputtered film thickness uniformity. Incorporating prior process knowledge into the GP framework, we show that our predictive models converge in fewer iterations compared to other modeling methods.