Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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A Study on Improving the Accuracy of Wafer Align Mark Center Detection Using Variable Thresholds

가변 Threshold를 이용한 Wafer Align Mark 중점 검출 정밀도 향상 연구

  • Hyeon Gyu Kim (Smart Manufacturing System R&D Department, Korea Institute of Industrial Technology) ;
  • Hak Jun Lee (Smart Manufacturing System R&D Department, Korea Institute of Industrial Technology) ;
  • Jaehyun Park (Smart Manufacturing System R&D Department, Korea Institute of Industrial Technology)
  • 김현규 (한국생산기술연구원 스마트생산시스템연구부문) ;
  • 이학준 (한국생산기술연구원 스마트생산시스템연구부문) ;
  • 박재현 (한국생산기술연구원 스마트생산시스템연구부문)
  • Received : 2023.11.28
  • Accepted : 2023.12.12
  • Published : 2023.12.31
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Abstract

Precision manufacturing technology is rapidly developing due to the extreme miniaturization of semiconductor processes to comply with Moore's Law. Accurate and precise alignment, which is one of the key elements of the semiconductor pre-process and post-process, is very important in the semiconductor process. The center detection of wafer align marks plays a key role in improving yield by reducing defects and research on accurate detection methods for this is necessary. Methods for accurate alignment using traditional image sensors can cause problems due to changes in image brightness and noise. To solve this problem, engineers must go directly into the line and perform maintenance work. This paper emphasizes that the development of AI technology can provide innovative solutions in the semiconductor process as high-resolution image and image processing technology also develops. This study proposes a new wafer center detection method through variable thresholding. And this study introduces a method for detecting the center that is less sensitive to the brightness of LEDs by utilizing a high-performance object detection model such as YOLOv8 without relying on existing algorithms. Through this, we aim to enable precise wafer focus detection using artificial intelligence.

Keywords

Acknowledgement

본 연구는 산업통상자원부 및 산업기술평가관리원(KEIT) 연구비 지원을 통해 수행되었습니다. (Nos. 20023103 and KM230314).

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