Autonomous Sputter Synthesis of Thin Film Nitrides with Composition Controlled by Bayesian Optimization of Optical Plasma Emission

Research output: Contribution to journalArticlepeer-review

Abstract

Autonomous experimentation has emerged as an efficient approach to accelerate the pace of material discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials, and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous workflow for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and a Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of plasma emission lines monitored during co-sputtering from elemental Zn and Ti targets in an N2 and Ar atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined compositions by minimizing the absolute error between desired and measured optical emission signals. We validated our approach by autonomously fabricating ZnxTi1−xNy films that deviate from the targeted cation composition by a relative ±3.5%, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with a specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity lines depend non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature.

Original languageAmerican English
Article numberArticle No. 071119
Number of pages9
JournalAPL Materials
Volume11
Issue number7
DOIs
StatePublished - 1 Jul 2023

Bibliographical note

Publisher Copyright:
© 2023 Author(s).

NREL Publication Number

  • NREL/JA-5K00-86172

Keywords

  • control systems
  • machine learning
  • optical emission spectroscopy
  • optimization algorithms
  • sputter deposition
  • thin films

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