@article{ART003153774},
author={Ju-Yong Cho},
title={Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2024},
volume={10},
number={6},
pages={15-21}
TY - JOUR
AU - Ju-Yong Cho
TI - Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space
JO - Journal of Internet of Things and Convergence
PY - 2024
VL - 10
IS - 6
PB - The Korea Internet of Things Society
SP - 15
EP - 21
SN - 2466-0078
AB - In response to the recent government's AI and semiconductor talent training policy, this study proposes a method of effectively classifying semiconductor chips and detecting defects in RGBA color space using AI deep learning technology. Quality assurance and defect detection of semiconductor chips are essential to ensure the reliability and performance of electronic devices. However, traditional inspection methods mainly include visual inspection, mechanical measurement, and electrical testing, which are time-consuming, expensive for state-of-the-art equipment, and inefficient for many production environments due to inspection. To solve this problem, image analysis techniques based on deep learning are attracting attention in automated inspection systems. Through this experiment, it was confirmed that the deep learning model using RGBA color space shows excellent performance in defect detection and classification of semiconductor chips. In particular, RGBA color space including alpha channel provides more accurate and precise results for defect detection than conventional RGB color space models with less learning. The results of this experiment suggest that the RGBA color space can play an important role in the deep learning-based defect detection system, and further experiments in various datasets and conditions will expand the scope of the method's use in the future. Such a model is highly likely to contribute to the automation and quality improvement of the semiconductor manufacturing process. This study aims to improve the accuracy and efficiency of the semiconductor chip inspection process by utilizing the advantages of RGBA color space.
KW - AI;Deep Learning;RGBA Color Space;Semiconductor Chip Classification;Defect Detection
DO -
UR -
ER -
Ju-Yong Cho. (2024). Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space. Journal of Internet of Things and Convergence, 10(6), 15-21.
Ju-Yong Cho. 2024, "Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space", Journal of Internet of Things and Convergence, vol.10, no.6 pp.15-21.
Ju-Yong Cho "Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space" Journal of Internet of Things and Convergence 10.6 pp.15-21 (2024) : 15.
Ju-Yong Cho. Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space. 2024; 10(6), 15-21.
Ju-Yong Cho. "Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space" Journal of Internet of Things and Convergence 10, no.6 (2024) : 15-21.
Ju-Yong Cho. Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space. Journal of Internet of Things and Convergence, 10(6), 15-21.
Ju-Yong Cho. Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space. Journal of Internet of Things and Convergence. 2024; 10(6) 15-21.
Ju-Yong Cho. Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space. 2024; 10(6), 15-21.
Ju-Yong Cho. "Research on Semiconductor Chip Classification and Defect Detection Using AI Deep Learning with RGBA Color Space" Journal of Internet of Things and Convergence 10, no.6 (2024) : 15-21.
