We introduce a Bayesian optimization approach to guide the sputter deposition
of molybdenum thin films, aiming to achieve desired residual stress and sheet
resistance while minimizing susceptibility to stochastic fluctuations during
deposition. Thin films are pivotal in numerous technologies, including
semiconductors and optical devices, where their properties are critical.
Sputter deposition parameters, such as deposition power, vacuum chamber
pressure, and working distance, influence physical properties like residual
stress and resistance. Excessive stress and high resistance can impair device
performance, necessitating the selection of optimal process parameters.
Furthermore, these parameters should ensure the consistency and reliability of
thin film properties, assisting in the reproducibility of the devices. However,
exploring the multidimensional design space for process optimization is
expensive. Bayesian optimization is ideal for optimizing inputs/parameters of
general black-box functions without reliance on gradient information. We
utilize Bayesian optimization to optimize deposition power and pressure using a
custom-built objective function incorporating observed stress and resistance
data. Additionally, we integrate prior knowledge of stress variation with
pressure into the objective function to prioritize films least affected by
stochastic variations. Our findings demonstrate that Bayesian optimization
effectively explores the design space and identifies optimal parameter
combinations meeting desired stress and resistance specifications.

我们引入了一种贝叶斯优化方法来指导钼薄膜的溅射沉积，旨在实现所需的残余应力和片电阻，同时最小化沉积过程中的随机波动的影响。