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

Plasma-enhanced atomic layer deposition (PEALD) is gaining interest in thin films for laser applications, and post-annealing treatments are often used to improve thin film properties. 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. For the refractive index, layer thickness, and O/Hf ratio, the THL-BPNN model achieved accuracy values of 0.99, 0.94, and 0.94, respectively, on the training set and 0.99, 0.91, and 0.90, respectively, on the validation set. The THL-BPNN model was further used to predict the laser-induced damage threshold of PEALD-HfO2 thin films and the properties of the PEALD-SiO2 thin films, both showing high accuracy. This study not only provides quantitative guidance for the improvement of thin film properties but also proposes a general model that can be applied to predict the properties of different types of laser thin films, saving experimental costs for process optimization.