Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system

Sanna Jarl, Jens Sjölund, Robert Frost, Anders Holst, Jonathan Scragg

Submission Type: I want my submission to be considered for both oral and poster presentation.

Keywords: Self-driving lab, PVD, Bayesian optimization, Gaussian processes, Active learning, Combinatorial thin films

TL;DR: This work advances an SDL based on magnetron co-sputtering. We present a fast, calibration-free, in-situ ML approach to predict the deposition rate using quartz-crystal microbalance sensors.

Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 1st 2025, I specify the DOI in the field below, and I submit the camera-ready version of the paper.

DOI: https://doi.org/10.1016/j.matdes.2025.115087

Submission Number: 37