Enhancing Quality Control: A Study on AI and Human Performance in Flip Chip Defect Detection

Enhancing Quality Control: A Study on AI and Human Performance in Flip Chip Defect Detection

This study introduces an advanced defect inspection model that utilizes object detection techniques to identify defects in Flip Chip cross-section images. The model serves as a valuable tool for failure analysis (FA) engineers working with Chip-on-Wafer (CoW) products by enhancing inspection precisi...

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Bibliographic Details
Main Authors: Wannisa Cheamsiri, Anuchit Jitpattanakul, Paisarn Muneesawang, Konlakorn Wongpatikaseree, Narit Hnoohom
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Flip chip defect
cross section
generative artificial intelligence
deep learning
object detection
YOLO models
Online Access:https://ieeexplore.ieee.org/document/10812716/
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https://ieeexplore.ieee.org/document/10812716/

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