Publications by authors named "Herbert Gruhn"
Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers.
ACS Appl Mater Interfaces
November 2021
Article Synopsis
- Atomic Layer Deposition (ALD) provides a high-quality, conformal coating ideal for protecting sensitive materials, but optimizing the deposition parameters is complex due to the high number of variables involved.
- Machine-learning methods, particularly Bayesian optimization (BO), have proven effective at minimizing defects in an ALD-AlO passivation layer for corrosion protection of copper, achieving optimal results in fewer than three trials.
- The study shows that with the optimized parameters, including surface pretreatment and specific deposition conditions, the corrosion resistance is significantly improved, highlighting the potential of integrating machine learning in materials science.
X-ray Diffraction and Spectro-Microscopic Study of ALD Protected Copper Films.
ACS Appl Mater Interfaces
July 2020
In many applications of copper in industry and research, copper migration and degradation of metallic copper to its oxides is a common problem. There are numerous ways to overcome this degradation with varying success. Atomic layer deposition (ALD) based encapsulation and passivation of the metallic copper recently emerged as a serious route to success owing to the conformality and density of the ALD films.
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