Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Deep Learning-Based Defect Detection in Cu-Cu Bonding Processes

  • DaBin Na (Department of Electronics Engineering, Myongji University) ;
  • JiMin Gu (Department of Electronics Engineering, Myongji University) ;
  • JiMin Park (Department of Electronics Engineering, Myongji University) ;
  • YunSeok Song (Department of Electronics Engineering, Myongji University) ;
  • JiHun Moon (Department of Electronics Engineering, Myongji University) ;
  • Sangyul Ha (Department of Semiconductor Engineering, Myongji University) ;
  • SangJeen Hong (Department of Semiconductor Engineering, Myongji University)
  • Received : 2024.06.08
  • Accepted : 2024.06.21
  • Published : 2024.06.30
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Abstract

Cu-Cu bonding, one of the key technologies in advanced packaging, enhances semiconductor chip performance, miniaturization, and energy efficiency by facilitating rapid data transfer and low power consumption. However, the quality of the interface bonding can significantly impact overall bond quality, necessitating strategies to quickly detect and classify in-process defects. This study presents a methodology for detecting defects in wafer junction areas from Scanning Acoustic Microscopy images using a ResNet-50 based deep learning model. Additionally, the use of the defect map is proposed to rapidly inspect and categorize defects occurring during the Cu-Cu bonding process, thereby improving yield and productivity in semiconductor manufacturing.

Keywords

Acknowledgement

This research was conducted with support from the Korea Industrial Technology Development Institute, under the Departmental Collaboration Semiconductor Major Track Project in 2024 (Project Number: G02P18800005 502). We would like to express our gratitude to researcher Hojeong Jeon for providing the copper bonding image data for this study.

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