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Leveraging Bayesian Optimization Software for Atomic Layer Deposition: Single-Objective Optimization of TiO₂ Layers.

Philipp Häussermann¹, Nikhil Biju Joseph², Daniel Hiller¹

¹Institute of Applied Physics (IAP), Technische Universität Bergakademie Freiberg, 09599 Freiberg, Germany.

Materials (Basel, Switzerland)

|October 26, 2024

Summary

Bayesian optimization (BO) software efficiently optimizes atomic layer deposition (ALD) processes. This machine learning approach enhances silicon surface passivation with titanium dioxide layers, reducing experimental costs and time.

Keywords:

Bayesian optimization (BO)atomic layer deposition (ALD)process optimization silicon surface passivation titanium dioxide (TiO2)

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Area of Science:

Materials Science
Chemical Engineering
Machine Learning

Background:

Atomic Layer Deposition (ALD) is crucial for thin-film fabrication.
Optimizing ALD processes traditionally requires extensive experimental design and resources.
Enhancing silicon surface passivation with materials like titanium dioxide is vital for device performance.

Purpose of the Study:

To demonstrate the application of Bayesian optimization (BO) software for streamlining ALD process optimization.
To improve the silicon surface passivation quality of titanium dioxide layers using BO.
To compare the efficiency of BO against classical experimental design methods.

Main Methods:

Utilized free-to-use Bayesian optimization software incorporating machine learning algorithms.
Applied BO to optimize the deposition of titanium dioxide layers using titanium tetraisopropoxide (TTIP).
Compared BO's adaptive search strategy with predefined methods like Box-Behnken and Plackett-Burman designs.

Main Results:

Achieved enhanced silicon surface passivation quality for titanium dioxide layers.
Demonstrated that BO requires fewer experimental runs compared to traditional methods.
Identified limitations due to constrained search spaces in single-objective optimization, highlighting the need for proper parameter bounds.

Conclusions:

Bayesian optimization offers a resource-efficient and time-saving alternative for ALD process optimization.
BO enables faster discovery of optimal ALD parameters, even with limited prior knowledge.
The adaptive nature of BO is particularly beneficial for small-scale laboratories with resource constraints.