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Autonomous experimentation has emerged as an efficient approach to acceleratethe pace of materials discovery. Although instruments for autonomous synthesishave become popular in molecular and polymer science, solution processing ofhybrid materials and nanoparticles, examples of autonomous tools for physicalvapor deposition are scarce yet important for the semiconductor industry. Here,we report the design and implementation of an autonomous workflow for sputterdeposition of thin films with controlled composition, leveraging a highlyautomated sputtering reactor custom-controlled by Python, optical emissionspectroscopy (OES), and a Bayesian optimization algorithm. We modeled filmcomposition, measured by x-ray fluorescence, as a linear function of emissionlines monitored during the co-sputtering from elemental Zn and Ti targets inN$_2$ atmosphere. A Bayesian control algorithm, informed by OES, navigates thespace of sputtering power to fabricate films with user-defined composition, byminimizing the absolute error between desired and measured emission signals. Wevalidated our approach by autonomously fabricating Zn$_x$Ti$_{1-x}$N$_y$ filmswith deviations from the targeted cation composition within relative 3.5 %,even for 15 nm thin films, demonstrating that the proposed approach canreliably synthesize thin films with specific composition and minimal humaninterference. Moreover, the proposed method can be extended to more difficultsynthesis experiments where plasma intensity depends non-linearly on pressure,or the elemental sticking coefficients strongly depend on the substratetemperature.

Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission