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An Algorithm Study to Detect Mass Flow Controller Error in Plasma Deposition Equipment Using Artificial Immune System  

You, Young Min (Department of Electronics Engineering, Myongji University)
Jeong, Ji Yoon (Department of Electronics Engineering, Myongji University)
Ch, Na Hyeon (Department of Electronics Engineering, Myongji University)
Park, So Eun (Department of Industrial and Management Engineering, Myongji University)
Hong, Sang Jeen (Department of Electronics Engineering, Myongji University)
Publication Information
Journal of the Semiconductor & Display Technology / v.20, no.4, 2021 , pp. 161-166 More about this Journal
Abstract
Errors in the semiconductor process are generated by a change in the state of the equipment, and errors usually arise when the state of the equipment changes or when parts that make up the equipment have flaws. In this investigation, we anticipated that aging of the mass flow controller in the plasma enhanced chemical vapor deposition SiO2 thin film deposition method caused a minute flow rate shift. In seven cases, fourier transformation infrared film quality analysis of the deposited thin film was used to characterize normal and pathological processes. The plasma condition was monitored using optical emission spectrometry data as the flow rate changed during the procedure. Preprocessing was used to apply the collected OES data to the artificial immune system algorithm, which was then used to process diagnosis. Through comparisons between datasets, the learning algorithm compared classification accuracy and improved the method. It has been confirmed that data characterized as a normal process and abnormal processes with differing flow rates may be discriminated by themselves using the artificial immune system data mining method.
Keywords
Semiconductor equipment; Artificial immune system; Mass flow controller; Error detection;
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