现代电子技术2025，Vol.48Issue(9)：86-92,7.DOI:10.16652/j.issn.1004-373x.2025.09.014
不确定性引导的芯片空洞分割网络
Uncertainty-guided cavity segmentation network for chip inspection
摘要
Abstract
Cavities that exist in welds can affect the air-tightness of chips seriously,which makes chip inspection be a crucial step in intelligent manufacturing.However,the multiscale shapes and uneven grayscale distribution of cavities pose great challenges to the deep-learning-based object segmentation models.To tackle these issues,this paper develops an uncertainty-guided cavity segmentation network(UGCSNet)for chip inspection.In the UGCSNet,a multi-scale feature extraction module is designed to improve the model representation capabilities for tiny-scaled cavities,and an uncertainty guidance module is proposed to enhance the model learning ability for the cavity edges.In addition,a channel attention mechanism is introduced to adaptively adjust the receptive field sizes of model and enhance the feature representation of specific semantics.In order to verify the effectiveness of the algorithm UGCSNet,experiments are carried out on the collected cavity datasets.Experimental results have shown that the proposed UGCSNet can improve the segmentation results of tiny cavities significantly.In comparison with the baseline U-Net,the UGCSNet achieves an improvement of IoU(intersection over union)for 3.4%and DICE for 5.0%.关键词
深度学习/图像分割/芯片检测/注意力机制/不确定性/空洞Key words
deep learning/image segmentation/chip inspection/attention mechanism/uncertainty/cavity分类
电子信息工程引用本文复制引用
侯云..不确定性引导的芯片空洞分割网络[J].现代电子技术,2025,48(9):86-92,7.基金项目
国家重点研发计划(2023YFB4707200) （2023YFB4707200）
国家自然科学基金项目(52175031) （52175031）
四川省自然科学基金项目(2023NSFSC0497) （2023NSFSC0497）
