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Neural network modeling and prediction of HfO2 thin film properties tuned by thermal annealing

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摘要 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 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.
出处 《High Power Laser Science and Engineering》 SCIE CAS CSCD 2024年第2期79-86,共8页 高功率激光科学与工程(英文版)
基金 This work was supported by the Program of Shanghai Academic Research Leader(No.23XD1424100) the CAS Project for Young Scientists in Basic Research(No.YSBR-081) the National Natural Science Foundation of China(No.61975215) the Science and Technology Planning Project of the Shanghai Municipal Science&Technology Commission(No.21DZ1100400).
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