基于主动学习和U-Net++分割的芯片封装空洞率的研究

doi:10.11896/jsjkx.220200092

Abstract

Abstract: Bump void is one of the most common physical defects in BGA packaging,which may lead to electrical failures and shortened lifetime.At present,the commonly used quality inspection is based on manual check on X-ray images,which has low accuracy and high time consumption.Therefore,automated chip detection methods based on deep learning draws increasing attention in industry.This paper proposes an active learning and U-Net+＋based void rate detection network.Based on active lear-ning,we apply equidistant partition for the whole dataset.For each sub-dataset,we take training-prediction-labeling-extension as pattern to optimize U-Net+＋network.The average dice coefficient on separated model sets reaches 80.99% on test set,while the overall accuracy rate reaches 94.89%.We innovatively apply active learning in in-line defect detection,and the result shows that,it can effectively enhance the labeling standard of data and model’s division precision.

Key words: Bump void, Active learning, Image segmentation, Object Detection

CLC Number:

TP399

QI Xuanlong, CHEN Hongyang, ZHAO Wenbing, ZHAO Di, GAO Jingyang. Study on BGA Packaging Void Rate Detection Based on Active Learning and U-Net++ Segmentation[J].Computer Science, 2023, 50(6A): 220200092-6.

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