Abstract
Atomic layer deposition (ALD) is an enabling technology for encapsulating sensitive materials owing to its high-quality, conformal coating capability. Finding the optimum deposition parameters is vital to achieving defect-free layers; however, the high dimensionality of the parameter space makes a systematic study on the improvement of the protective properties of ALD films challenging. Machine-learning (ML) methods are gaining credibility in materials science applications by efficiently addressing these challenges and outperforming conventional techniques. Accordingly, this study reports the ML-based minimization of defects in an ALD-Al2O3 passivation layer for the corrosion protection of metallic copper using Bayesian optimization (BO). In all experiments, BO consistently minimizes the layer defect density by finding the optimum deposition parameters in less than three trials. Electrochemical tests show that the optimized layers have virtually zero film porosity and achieve five orders of magnitude reduction in corrosion current as compared to control samples. Optimized parameters of surface pretreatment using Ar/H2 plasma, the deposition temperature above 200 °C, and 60 ms pulse time quadruple the corrosion resistance. The significant optimization of ALD layers presented in this study demonstrates the effectiveness of BO and its potential outreach to a broader audience, focusing on different materials and processes in materials science applications.
Highlights
The utilization of machine-learning (ML)-based approaches has recently gained significant momentum in addressing the challenging problems in materials science and engineering.[1]
In our previous study performed on copper substrates, we investigated the corrosion protection of Al2O3/TiO2 and Al2O3/SiO2 bilayers at elevated temperatures.[47]
We studied the properties of the fabricated thin films using wet-chemical etching, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), focused ion beam (FIB), and linear sweep voltammetry (LSV) methods
Summary
The utilization of machine-learning (ML)-based approaches has recently gained significant momentum in addressing the challenging problems in materials science and engineering.[1]. In addition to the exploration of new materials, complex material fabrication processes (e.g., thin-film deposition) that depend on a careful selection of fabrication parameters can benefit from the ML methods, which can generate optimum results in a time- and cost-effective fashion. Such an ML-based optimization approach was applied to a technologically very relevant problem: the encapsulation of copper against chemical attacks from a harsh environment
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