Elevating wafer defect inspection with denoising diffusion probabilistic model

College

Gokongwei College of Engineering

Department/Unit

Center for Engineering and Sustainable Development Research

Document Type

Article

Source Title

Mathematics

Volume

12

Publication Date

2024

Abstract

Integrated circuits (ICs) are critical components in the semiconductor industry, and precise wafer defect inspection is essential for maintaining product quality and yield. This study addresses the challenge of insufficient sample patterns in wafer defect datasets by using the denoising diffusion probabilistic model (DDPM) to produce generated defects that elevate the performance of wafer defect inspection. The quality of the generated defects was evaluated using the Fréchet Inception Distance (FID) score, which was then synthesized with real defect-free backgrounds to create an augmented defect dataset. Experimental results demonstrated that the augmented defect dataset significantly boosted performance, achieving 98.7% accuracy for YOLOv8-cls, 95.8% box mAP for YOLOv8-det, and 95.7% mask mAP for YOLOv8-seg. These results indicate that the generated defects produced by the DDPM can effectively enrich wafer defect datasets and enhance wafer defect inspection performance in real-world applications.

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Disciplines

Manufacturing

Keywords

Integrated circuits—Defects; Semiconductor wafers—Defects

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