In Situ X-ray Diffraction and Spectro-Microscopic Study of ALD Protected Copper Films

Iterative reverse Monte Carlo and molecular statics for improved atomic structure modeling: a case study of zinc oxide grown by atomic layer deposition

Reverse Monte Carlo (RMC) modeling is a common method to derive atomic structure models of materials from experimental diffraction data. However, conventional RMC modeling does not impose energetic constraints and can produce non-physical local structures within the simulation volume. Although previous strategies have introduced energetic constraints during RMC modeling, these approaches have limitations in computational cost and physical accuracy. In this work, we periodically introduce molecular statics (MS) energy minimizations during RMC modeling in an iterative RMC-MS approach. We test this iterative RMC-MS approach using diffraction data collected by in operando high energy X-ray diffraction during atomic layer deposition of ZnO as a sample case. For MS relaxations we employ ReaxFF pair potentials previously established for ZnO. We find that RMC-MS and RMC provide equivalent agreement with experimental data, but RMC-MS structures are on average 0.6 eV per atom lower in energy and are more consistent with known ZnO atomic structure features. The iterative RMC-MS approach we report can accommodate large systems with minimal additional computational burden beyond a standard RMC simulation and can leverage established pair potentials for immediate application to study a wide range of materials.

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

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.