Preliminary communication

https://doi.org/10.31803/tg-20230425154156

Leveling Maintenance Mechanism by Using the Fabry-Perot Interferometer with Machine Learning Technology

Syuan-Cheng Chang orcid.org/0000-0003-3258-4381 ; National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Chung-Ping Chang ; National Chiayi University, 300 Syuefu Road, Chiayi 600355, Taiwan
Yung-Cheng Wang ; National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
Chi-Chieh Chu ; National Chiayi University, 300 Syuefu Road, Chiayi 600355, Taiwan

Abstract

This study proposes a method for maintaining parallelism of the optical cavity of a laser interferometer using machine learning. The Fabry-Perot interferometer is utilized as an experimental optical structure in this research due to its advantage of having a brief optical structure. The supervised machine learning method is used to train algorithms to accurately classify and predict the tilt angle of the plane mirror using labeled interference images. Based on the predicted results, stepper motors are fixed on a plane mirror that can automatically adjust the pitch and yaw angles. According to the experimental results, the average correction error and standard deviation in 17-grid classification experiment are 32.38 and 11.21 arcseconds, respectively. In 25-grid classification experiment, the average correction error and standard deviation are 19.44 and 7.86 arcseconds, respectively. The results show that this parallelism maintenance technology has essential for the semiconductor industry and precision positioning technology.

Keywords

Fabry-Perot interferometer; interference image; leveling maintenance; machine learning; optical measurement

Hrčak ID:

301550

URI

https://hrcak.srce.hr/301550

Publication date:

15.6.2023.

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