Wafer-View Defect-Pattern-Prominent GDBN Method Using MetaFormer Variant

Shu-Wen Li, Chia-Heng Yen, Shuo-Wen Chang, Ying-Hua Chu, Kai-Chiang Wu, Mango Chia-Tso Chao

Abstract: Good-Die-in-Bad-Neighborhood (GDBN) is a technique employed to identify chips that pass initial tests but may have defects. Previous research used neural networks and expanded observation windows but ignored the impact of isolated dice. This paper improves wafer pattern information through denoising and creates a lightweight model. It also reduces training time by annotating multiple dice simultaneously. Experiments on real-world datasets show the model effectively captures more Test Escapes, reducing Defective Parts Per Million (DPPM) and improving return merchandise authorization gains.