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Intelligent Agents for the Optimization of Atomic Layer Deposition.

Noah H Paulson1, Angel Yanguas-Gil1, Osama Y Abuomar2

  • 1Applied Materials Division, Argonne National Laboratory, Argonne, Illinois 60439, United States.

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Summary

Developing efficient optimization strategies for Atomic Layer Deposition (ALD) is crucial. This study presents three novel approaches to autonomously determine optimal ALD processing conditions, even with noisy experimental data.

Keywords:
Bayesian optimizationartificial intelligenceatomic layer depositionexpert systemsprocess optimization

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Nanotechnology

Background:

  • Atomic Layer Deposition (ALD) enables precise thin film synthesis for computing, energy, and separation technologies.
  • The vast chemical and process diversity in ALD presents challenges in identifying optimal parameters for stable, uniform film growth and minimal precursor use.
  • Current ALD process development relies on time-consuming trial-and-error, often hindered by experimental noise and complex surface chemistries.

Purpose of the Study:

  • To develop and evaluate efficient, autonomous optimization strategies for ALD process development.
  • To address the challenges of identifying optimal ALD processing parameters amidst experimental noise and complex surface chemistries.
  • To accelerate the discovery of processing conditions for stable, uniform ALD films with minimal precursor consumption.

Main Methods:

  • Development of three distinct optimization strategies for ALD process parameter identification.
  • Simulation of four different ALD processes to test and compare the performance of the developed strategies.
  • Investigation into the impact of experimental noise in growth per cycle (GPC) measurements on optimization convergence.

Main Results:

  • Successful development of three novel optimization strategies for ALD.
  • Comparative analysis demonstrating the performance of these strategies across simulated ALD processes.
  • Quantification of the effect of GPC measurement noise on the convergence of optimization algorithms.

Conclusions:

  • The developed optimization strategies offer a more efficient and autonomous approach to ALD process development.
  • These methods can accelerate the identification of optimal ALD conditions, reducing experimental effort and precursor waste.
  • Understanding the impact of noise is critical for robust ALD process optimization.