Deep learning technique for generating sparse defects to improve image classification performance

Dahyun Park; Jaehyeon Cho; So-myeong Ahn; Kyunghwan Moon; Youngmin Hwang; Hyojin Lee

doi:10.1117/12.2673486

11 August 2023 Deep learning technique for generating sparse defects to improve image classification performance

Dahyun Park, Jaehyeon Cho, So-myeong Ahn, Kyunghwan Moon, Youngmin Hwang, Hyojin Lee

Proceedings Volume 12623, Automated Visual Inspection and Machine Vision V; 126230J (2023) https://doi.org/10.1117/12.2673486
Event: SPIE Optical Metrology, 2023, Munich, Germany

Conference Poster

Abstract

Display and semiconductor manufacturing require inspection and repair process steps to increase the final product yield. To this end, it is necessary to divide into normal and defective images based on display and semiconductor images taken through an optical camera. This is a simple binary classification problem, but for the repair process, a more detailed classification technique is required. In order to automate this and solve it through deep learning, it is necessary to collect enough training data for each class. However, there are problems with certain defective classes that the deep learning model can't get enough to train. This greatly delays the time to apply the classification algorithm to the field, which adversely affects product mass production. In this paper, by using the deep learning method, sparse defective class images are naturally created, contributing to improving the performance of the final classification model. In addition, it is confirmed through experiments that artificially created images are made with the same shape and characteristics as non-made images of the same class.

Citation Download Citation

Dahyun Park, Jaehyeon Cho, So-myeong Ahn, Kyunghwan Moon, Youngmin Hwang, and Hyojin Lee "Deep learning technique for generating sparse defects to improve image classification performance", Proc. SPIE 12623, Automated Visual Inspection and Machine Vision V, 126230J (11 August 2023); https://doi.org/10.1117/12.2673486

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