Machine learning assisted CALPHAD framework for thermodynamic analysis of CVD SiOxNy thin films

IF 1.9 3区材料科学 Q4 CHEMISTRY, PHYSICAL

Calphad-computer Coupling of Phase Diagrams and Thermochemistry Pub Date : 2025-02-09 DOI:10.1016/j.calphad.2025.102806

Junjun Wang , Bingquan Xu , Kyungjun Lee , Wei Huang , Huihui Wang , Jian Peng , Man Xu

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引用次数: 0

Abstract

A machine learning assisted CALPHAD framework is applied in this study to thermodynamically analyze the chemical vapor deposition (CVD) process for silicon oxynitride films. Among the various machine learning algorithms evaluated, Random Forest (RF) was identified as the optimal model due to its superior accuracy and generalization performance. The study identified that only 5 % data of the original dataset is required to effectively train the RF model. The best-trained RF model can excellently reproduce results from CALPHAD. SHAP analysis was performed to quantify the contribution of input features to the performance of machine learning model. The results revealed that NH₃/N₂O and NH₃/SiCl₄ ratios have the most significant influence on the mole fractions of SiO₂ and Si₂N₂O, while the NH₃/N₂O ratio is the dominant factor affecting the mole fraction of Si₃N₄ in the deposit. Notably, the ML-assisted CALPHAD framework demonstrated a 20-fold increase in analysis efficiency compared to traditional CALPHAD calculations.

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来源期刊

Calphad-computer Coupling of Phase Diagrams and Thermochemistry 化学-热力学

CiteScore

4.00

自引率

16.70%

发文量

审稿时长

2.5 months

期刊介绍： The design of industrial processes requires reliable thermodynamic data. CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) aims to promote computational thermodynamics through development of models to represent thermodynamic properties for various phases which permit prediction of properties of multicomponent systems from those of binary and ternary subsystems, critical assessment of data and their incorporation into self-consistent databases, development of software to optimize and derive thermodynamic parameters and the development and use of databanks for calculations to improve understanding of various industrial and technological processes. This work is disseminated through the CALPHAD journal and its annual conference.