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.

等离子体增强原子层沉积（Plasma-enhanced atomic layer deposition, PEALD）在面向激光应用的薄膜领域日益受到关注，而后退火处理常被用于改善薄膜性能。然而，当前用于优化薄膜性能的研究往往依赖成本高昂且耗时的试错流程。本研究制备了PEALD-HfO₂薄膜样品，并在不同退火气氛与温度下对其进行处理。随后从折射率、膜层厚度以及氧铪比（O/Hf ratio）三个维度对样品开展表征分析。将采集得到的数据集划分为训练集与验证集，输入至多款带有不同隐藏层数量的反向传播神经网络（back-propagation neural networks, BPNNs）中，以探索构建工艺-性能关联关系的最优方案。研究结果表明，三层隐藏层反向传播神经网络（three-hidden-layer back-propagation neural network, THL-BPNN）实现了稳定且精准的拟合效果。针对折射率、膜层厚度与氧铪比三项指标，THL-BPNN模型在训练集上的拟合精度分别为0.99、0.94与0.94，在验证集上则分别达到0.99、0.91与0.90。进一步将THL-BPNN模型用于预测PEALD-HfO₂薄膜的激光诱导损伤阈值，以及PEALD-SiO₂薄膜的性能，均取得了较高的预测精度。本研究不仅为薄膜性能优化提供了量化指导，还提出了可用于预测不同类型激光薄膜性能的通用模型，可为工艺优化实验节省大量成本。