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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Kyunghan Chun
Daegu Catholic University
Computer Science & IT Electrical & Electronics Engineering

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Non-destructive Inspection System Development for Secondary Battery Welding Part Kyunghan Chun
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.4177

Abstract

In this paper, we develop a non-destructive inspection system for secondary battery welding. In a secondary battery, when the insulation or separator of each electrode is damaged by an impact from the outside depending on the degree of welding, an internal short circuit occurs during charging and fires even if it does not ignite at that time. To detect this, a non-destructive AOI (Automatic Optical Inspection) system is developed that compares the inspection target with the reference image to determine whether there is a proper indentation in the welding part. The system consists of a precision alignment stage on the lower part and imaging equipment that performs AOI, a non-destructive inspection on the upper part. And the appropriate exposure, i.e., the aperture setting of the used camera, was confirmed through the experiment according to the position of the pole.
Deep Learning Method for Wafer Flaw Detection in Lab-level Photolithography Subin Lee; Kyunghan Chun
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7975

Abstract

In this paper, we propose a deep learning-based method for wafer flaw detection and classification in lab-label photolithography, known as a core step of the semiconductor manufacturing process. In photolithography, defects due to particles or process errors are critical to product yield and reliability. To detect these flaws, images were collected and efficientnet deep learning method was applied. Data augmentation and model lightweighting techniques were also applied to improve the limitations of the dataset. experimental results showed the relation between model complexity and the amount of training data. For EfficientNetB5, the massive architecture, caused the overfitting problem because of learning even noise in small datasets. But EfficientNetB0, the lightweight model, with batch normalization and early stopping techniques shows improvement of the reliability. In conclusion, this study provides practical guidelines for building and efficient flaw detection method in a data-limited research environment.
Co-Authors Subin Lee