Neural Network Modeling and Prediction of HfO2 Thin Film Properties Tuned by Thermal Annealing

However, research to improve thin-film properties is often based on an expensive and time-consuming trial-and-error process. In this study, PEALD-HfO2 thin film samples were deposited and treated under different annealing atmospheres and temperatures.

The samples were characterized in terms of their refractive indices, layer thicknesses, and O/Hf ratios. The collected data were split into training and validation sets and fed to multiple back-propagation neural networks (BPNNs) with different hidden layers to determine the best way to construct the process-performance relationship. The results showed that the three-hidden-layer back-propagation neural network (THL-BPNN) achieved stable and accurate fitting.