Research on Internal Defect Annotation and Detection Methods of Semiconductor Devices
Internal void defects that occur during the packaging process of semiconductor devices directly affect the performance of electronic devices.Aiming at the problems of different sizes of void defects in internal X-ray images of semiconductor devices and the difficulty in labeling,positioning,and noise interference,a semi-automatic annotation method and a U-Net-based device internal void defect detection method are proposed.The semiautomatic annotation method uses threshold segmentation to initially locate the defect area,generate the bounding rectangle of the defect,and then manually modify and improve the rectangular frame,which is input into the Segment Anything Model(SAM)as a prompt to obtain high-precision segmentation results.Semiautomatic labeling methods can reduce labeling time,improve label quality,and overcome labeling problems.To address the poor generalization of the classic U-Net method,an improved U-Net method(EFU-Net)is proposed.First,an Edge and Position Enhancement(EPE)module is introduced into the encoder.By combining the Sobel filter and coordinate attention mechanism,it enhances the perception of image edge information and effectively integrates position information to improve the accuracy of feature extraction.The Feature Fusion Control(FFC)module is introduced to replace the traditional skip connection,fuse the three features of high-level features,low-level features,and prediction mask,and utilize multilayer parallel atrous convolution and an attention force gating mechanism to enable more targeted and high-quality feature fusion.The experimental results on the semiconductor device dataset show that the Dice coefficient and MIoU of EFA-NET reach 70.71％and 77.23％,respectively,which are improvements of 14 and 7.71,respectively,percentage points compared with the U-Net method and exhibit better segmentation performance.
defect detectionsemantic segmentationsemi-automatic annotationEdge Position Enhancement(EPE)attention mechanismFeature Fusion Control(FFC)
万方数据
维普
