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Procedure for the Transfer of Polymer Films Onto Porous Substrates with Minimized Defects

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Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers.

Gül Dogan^1,2, Sinan O Demir², Rico Gutzler³

¹Robert Bosch GmbH, Automotive Electronics, Postfach 13 42, 72703 Reutlingen, Germany.

ACS Applied Materials & Interfaces

|November 4, 2021

Summary

This summary is machine-generated.

Machine learning, specifically Bayesian optimization, efficiently minimized defects in aluminum oxide (Al2O3) coatings for copper corrosion protection. Optimized atomic layer deposition parameters significantly enhanced corrosion resistance, demonstrating a powerful approach for materials science.

Keywords:

Bayesian optimization atomic layer deposition copper defect density wet etching

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Area of Science:

Materials Science
Surface Chemistry
Corrosion Engineering

Background:

Atomic Layer Deposition (ALD) provides high-quality conformal coatings crucial for material encapsulation.
Optimizing ALD parameters for defect-free layers is challenging due to high dimensionality.
Machine Learning (ML) offers efficient solutions for complex materials science problems.

Purpose of the Study:

To employ ML, specifically Bayesian Optimization (BO), for minimizing defects in ALD-Al2O3 passivation layers.
To enhance the corrosion protection of metallic copper using optimized ALD parameters.
To demonstrate the effectiveness of BO in accelerating materials optimization.

Main Methods:

Utilized Bayesian Optimization (BO) to systematically explore and identify optimal ALD deposition parameters.
Performed electrochemical tests to evaluate the corrosion resistance and film properties of ALD-Al2O3 layers.
Investigated the impact of surface pretreatment (Ar/H2 plasma), deposition temperature, and pulse time on layer quality and performance.

Main Results:

BO successfully minimized defect density in ALD-Al2O3 layers within three trials.
Optimized layers exhibited near-zero film porosity and a five-order-of-magnitude reduction in corrosion current.
Key parameters included Ar/H2 plasma pretreatment, deposition temperatures above 200 °C, and 60 ms pulse time, which quadrupled corrosion resistance.

Conclusions:

Bayesian Optimization is a highly effective tool for optimizing ALD processes and reducing defects in passivation layers.
The optimized ALD-Al2O3 coatings provide exceptional corrosion protection for metallic copper.
This ML-driven approach has broad applicability for materials development across various scientific domains.