Alan J. H. McGaughey

The impact of rapid thermal annealing (1000 °C for 1 min) on the thermal transport properties of amorphous alumina (a-Al2O3) thin films grown by atomic layer deposition on β−Ga2O3 and amorphous silica (a-SiO2) substrates is determined using frequency-domain thermoreflectance measurements. The annealing more than doubles the a-Al2O3 thermal conductivity for both substrates (1.54 ± 0.13 to 3.14 ± 0.27 W m−1 K−1 for β−Ga2O3 and 1.60 ± 0.14 to 3.87 ± 0.33 W m−1 K−1 for a-SiO2) while keeping the film amorphous. The thermal conductivity increase is attributed to partial recrystallization and off-gassing of embedded impurities. Annealing halves the thermal boundary resistance of the a-Al2O3/a-SiO2 interface (10.5 ± 1.0 to 4.47 ± 0.42 m2 K GW−1), which is attributed to compositional mixing and structural reorganization that are enabled by the elastic matching of these two materials. The thermal boundary resistance of the a-Al2O3/β−Ga2O3 interface is not affected by annealing due to the elastic mismatch. Reducing the thermal resistance of a-Al2O3 dielectric films and adjacent interfaces by annealing will promote lateral heat spreading adjacent to hot spots and improve device longevity.

Feature extraction and a neural network model are applied to predict defect types and concentrations in experimental anatase TiO2 samples. A dataset of TiO2 structures with vacancies and interstitials of oxygen and titanium is built, and the structures are relaxed using energy minimization. The features of the calculated pair distribution functions (PDFs) of these defected structures are extracted using linear methods (principal component analysis and non-negative matrix factorization) and non-linear methods (autoencoder and convolutional neural network). The extracted features are used as inputs to a neural network that maps feature weights to the concentration of each defect type. The performance of this machine learning pipeline is validated by predicting defect concentrations based on experimentally measured TiO2 PDFs and comparing the results to brute-force predictions. A physics-based initialization of the autoencoder has the highest accuracy in predicting defect concentrations. This model incorporates physical interpretability and predictability of material structures, enabling a more efficient characterization process with scattering data.