Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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AI/BIG DATA-based Smart Factory Technology Status Analysis for Effective Display Manufacturing

효과적인 디스플레이 제조를 위한 AI/BIG DATA 기반 스마트 팩토리 기술 현황 분석

  • Jung, Sukwon (Division of Computer and Information Engineering, Hoseo University) ;
  • Lim, Huhnkuk (Division of Computer and Information Engineering, Hoseo University)
  • Received : 2020.12.18
  • Accepted : 2021.02.15
  • Published : 2021.03.31
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Abstract

In the field of display, a smart factory means more efficient display manufacturing using AI/BIG DATA technology not only for job automation, but also for existing process management, moving facilities, process abnormalities, and defect classification. In the past, when defects appeared in the display manufacturing process, the classification of defects and coping with process abnormalities were different, a lot of time was consumed for this. However, in the field of display manufacturing, advanced process equipment must be used, and it can be said that the competitiveness of the display manufacturing industry is to quickly identify the cause of defects and increase the yield. In this paper, we will summarize the cases in which smart factory AI/BIG DATA technology is applied to domestic display manufacturing, and analyze what advantages can be derived compared to existing methods. This information can be used as prior knowledge for improved smart factory development in the field of display manufacturing using AI/BIG DATA.

디스플레이 분야에 스마트 팩토리란 작업 자동화 뿐만 아니라 기존의 공정관리, 이동설비, 공정이상, 결함 분류 등에 AI/BIG DATA 기술을 이용한 보다 효율적인 디스플레이 제조를 의미한다. 과거 디스플레이 제조 과정에서 불량이 나오면 결함 분류, 공정 이상에 대한 대처가 시시각각 달랐기 때문에 이에 대한 많은 시간 소모가 발생했었다. 하지만 디스플레이 제조 분야는 고도화된 공정 장비를 이용해야 하고 불량 원인을 신속하게 파악해 수율을 올리는 것이 디스플레이 제조 산업의 경쟁력이다. 본 논문에는 스마트 팩토리 AI/BIG DATA 기술을 디스플레이 제조에 접목한 사례들에 대해 정리해 보고 기존 방법 대비 어떤 장점이 도출 되어질 수 있는지에 대해 처음으로 분석해 보고자 한다. 이를 통해 향후 AI/BIG DATA를 이용한 디스플레이 제조 분야에 보다 향상된 스마트 팩토리 개발을 위한 사전지식으로 활용하고자 한다.

Keywords

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