Analysis and prediction of sputtering yield using combined hierarchical clustering analysis and artificial neural network algorithms

• Published in: Plasma science & technology Vol. 26; no. 11; pp. 115504 - 115511
• Main Authors: CHEN, Yu, LUO, Jiawei, LEI, Wen, SHEN, Yan, CAO, Shuai
• Format: Journal Article
• Language: English
• Published: Plasma Science and Technology 01.11.2024
• Subjects: machine learning; plasma; sputtering
• ISSN: 1009-0630
• DOI: 10.1088/2058-6272/ad709c

Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E th , maximum sputtering energy E max and maximum sputtering yield SY max . In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors ( R 2 evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R 2 of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.