Abstract
We demonstrate the application of free-to-use and easy-to-implement Bayesian optimization (BO) software to streamline atomic layer deposition (ALD) process optimization. By employing machine learning-based Bayesian optimization algorithms, we enhanced the silicon surface passivation quality of titanium dioxide layers deposited using titanium tetraisopropoxide (TTIP). Unlike classical designs of experimental methods, such as Box-Behnken or Plackett-Burman designs, which require a predefined set of experiments and can become resource intensive, BO offers several advantages. It dynamically updates the search strategy based on previous outcomes, allowing for efficient exploration of parameter spaces with fewer experimental runs. This adaptive approach is particularly advantageous in small-scale experiments or laboratories where time, resources, and materials are limited. In a single-objective optimization experiment, we identified constrained search spaces that limited further optimization, underscoring the importance of properly defined parameter bounds prior to the optimization process. Our findings highlight that Bayesian optimization can not only reduce time and resource costs associated with ALD process optimization but also support faster discovery of more optimal ALD process parameters, even with minimal prior knowledge of the deposition process or precursor chemistry.