A Novel End-to-End Deep Learning Framework for Chip Packaging Defect Detection

Author:

Zhou Siyi12,Yao Shunhua3,Shen Tao12,Wang Qingwang12ORCID

Affiliation:

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

2. Yunnan Key Laboratory of Computer Technologies Application, Kunming University of Science and Technology, Kunming 650500, China

3. Wuxi Aiyingna Electromechanical Equipment Co., Ltd., Wuxi 214028, China

Abstract

As semiconductor chip manufacturing technology advances, chip structures are becoming more complex, leading to an increased likelihood of void defects in the solder layer during packaging. However, identifying void defects in packaged chips remains a significant challenge due to the complex chip background, varying defect sizes and shapes, and blurred boundaries between voids and their surroundings. To address these challenges, we present a deep-learning-based framework for void defect segmentation in chip packaging. The framework consists of two main components: a solder region extraction method and a void defect segmentation network. The solder region extraction method includes a lightweight segmentation network and a rotation correction algorithm that eliminates background noise and accurately captures the solder region of the chip. The void defect segmentation network is designed for efficient and accurate defect segmentation. To cope with the variability of void defect shapes and sizes, we propose a Mamba model-based encoder that uses a visual state space module for multi-scale information extraction. In addition, we propose an interactive dual-stream decoder that uses a feature correlation cross gate module to fuse the streams’ features to improve their correlation and produce more accurate void defect segmentation maps. The effectiveness of the framework is evaluated through quantitative and qualitative experiments on our custom X-ray chip dataset. Furthermore, the proposed void defect segmentation framework for chip packaging has been applied to a real factory inspection line, achieving an accuracy of 93.3% in chip qualification.

Funder

Yunnan Fundamental Research Projects

National Natural Science Foundation of China

Major Science and Technology Projects in Yunnan Province

Publisher

MDPI AG

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