High Power Laser Science and Engineering, Volume. 12, Issue 2, 02000e21(2024)
Neural network modeling and prediction of HfO
Figures & Tables(10)
![THL-BPNN model with all neurons in adjacent layers connected, where x = [x1; x2], y1 and hij represent the input, output and intermediate processing signals, respectively.](/Images/icon/loading.gif){kind=icon}
Fig. 1. THL-BPNN model with all neurons in adjacent layers connected, where
Fig. 2. Accuracy of BPNNs with one to four hidden layers based on (a) the refractive index (at 355 nm), (b) layer thickness and (c) O/Hf ratio of PEALD-HfO2. The four columns in each subgraph represent the
Fig. 3. Measured and predicted (a)–(c) refractive index, (d)–(f) layer thickness and (g)–(i) O/Hf ratio of HfO2 thin films. The data in the left-hand, middle and right-hand columns are predicted by the LR model, SVR model and THL-BPNN model, respectively. The blue line (with a slope of 1) serves as a guideline for perfect prediction.
Fig. 4. Correlations between properties of HfO2 thin films used in this section. Blue indicates a negative correlation, whereas red indicates a positive correlation. Darker colors and larger circles indicate higher correlations. The numbers inside the circles indicate the corresponding correlation coefficients of the two features.
Fig. 5. Comparison of measured and predicted LIDT values on the (a) training set and (b) validation set.
Fig. 6. Comparison of measured and predicted values of (a) the refractive index (at 355 nm), (b) the layer thickness and (c) the O/Si ratio for SiO2 thin films in the validation set.
Table 1. Datasets for property prediction of HfO2 and SiO2 thin films.
View tableView in ArticleTable 1. Datasets for property prediction of HfO2 and SiO2 thin films.
HfO2 thin films SiO2 thin films Variables Range Variables Range Input Annealing atmosphere* 0–3 Deposition temperature (°C) 50–200 Annealing temperature (°C) 0–800 Precursor exposure time (s) 0.1–0.7 Output Refractive index (at 355 nm) 1.83–2.24 Refractive index (at 355 nm) 1.48–1.49 Thickness (nm) 34.7–50.3 Thickness (nm) 69.0–88.1 O/Hf ratio 1.80–2.04 O/Si ratio 1.94–2.01
Table 2. Datasets for LIDT prediction of HfO2 and SiO2 thin films.
View tableView in ArticleTable 2. Datasets for LIDT prediction of HfO2 and SiO2 thin films.
Range Variables HfO2 SiO2 thin films thin films Input Type* 1 2 Total impurity content 5.4–13.5 0.6–1.1 (%, atomic fraction) Absorption (ppm) 211–10892 3.8–5.8 Stoichiometric ratio 1.81–2.06 1.94–2.01 Output LIDT (J/cm2) 1.2–6.3 22.0–39.4
Table 3. Evaluation of the LR, SVR and THL-BPNN models.
View tableView in ArticleTable 3. Evaluation of the LR, SVR and THL-BPNN models.
Refractive index Layer thickness O/Hf ratio Training data Validation data Training data Validation data Training data Validation data R2 RMSE R2 RMSE R2 RMSE R2 RMSE R2 RMSE R2 RMSE LR 0.72 0.06 0.66 0.08 0.74 2.24 0.48 2.88 0.43 0.08 0.48 0.08 SVR 0.71 0.06 0.52 0.10 0.75 2.22 0.56 2.64 0.84 0.04 0.74 0.05 THL-BPNN 0.99 0.01 0.99 0.01 0.94 1.08 0.91 1.18 0.94 0.03 0.90 0.03
Table 4. Evaluation of the THL-BPNN model for SiO2 thin film properties.
View tableView in ArticleTable 4. Evaluation of the THL-BPNN model for SiO2 thin film properties.
Training data Validation data R2 RMSE AA RMSE Refractive index 0.99 0.00 1.00 0.00 Layer thickness 0.99 0.43 0.98 1.72 O/Si ratio 0.81 0.01 0.99 0.03
Tools
Get Citation
Copy Citation Text
Min Gao, Chaoyi Yin, Jianda Shao, Meiping Zhu. Neural network modeling and prediction of HfO
Paper Information
Category: Research Articles
Received: Aug. 10, 2023
Accepted: Feb. 6, 2024
Published Online: May. 7, 2024
The Author Email: Meiping Zhu (bree@siom.ac.cn)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20