Detecting and Measuring Defects in Wafer Die Using GAN and YOLOv3

Ssu-Han Chen,Chih-Hsiang Kang,Der-Baau Perng

doi:10.3390/app10238725

Ssu-Han Chen, Chih-Hsiang Kang + Show 1 more

Open Access

https://doi.org/10.3390/app10238725

Copy DOI

Abstract

This research used deep learning methods to develop a set of algorithms to detect die particle defects. Generative adversarial network (GAN) generated natural and realistic images, which improved the ability of you only look once version 3 (YOLOv3) to detect die defects. Then defects were measured based on the bounding boxes predicted by YOLOv3, which potentially provided the criteria for die quality sorting. The pseudo defective images generated by GAN from the real defective images were used as the training image set. The results obtained after training with the combination of the real and pseudo defective images were 7.33% higher in testing average precision (AP) and more accurate by one decimal place in testing coordinate error than after training with the real images alone. The GAN can enhance the diversity of defects, which improves the versatility of YOLOv3 somewhat. In summary, the method of combining GAN and YOLOv3 employed in this study creates a feature-free algorithm that does not require a massive collection of defective samples and does not require additional annotation of pseudo defects. The proposed method is feasible and advantageous for cases that deal with various kinds of die patterns.

Highlights

Wafer is the major material for making integrated circuits (ICs), and it plays an indispensable role in electronic products
When we directly input a set of die images into the Generative adversarial network (GAN) model, we found that its objective function value fluctuated during the iteration and converged only with difficulty
The testing average precision (AP) was used to measure the performance of the predicted bounding boxes: after the testing image set was inferred by the object detection method, the predicted bounding boxes were compared with the ground truth boxes, and the average of the maximum precision values calculated process first inferred the predicted bounding boxes of the defects through you only look once version 3 (YOLOv3)

Summary

Introduction

Wafer is the major material for making integrated circuits (ICs), and it plays an indispensable role in electronic products. The upstream of the semiconductor industry are IC design companies and silicon wafer manufacturing companies. IC design companies design circuit diagrams according to customer needs, while silicon wafer manufacturing companies use polysilicon as the raw material for silicon wafers. The primary task of IC manufacturing companies in the midstream is to transfer the circuit diagrams to wafers. The completed wafer is sent to the downstream IC packaging and testing companies for packaging and testing the functions of ICs, concluding the whole manufacturing process. With the continuous evolution of wafer manufacturing technology, wafer sizes have become larger and the patterns on the die have become more diverse. In order to inspect surface defects in the dies of a wafer, automated optical inspection (AOI), mainly using one or more optical imagery charge-coupled devices (CCDs), has gradually replaced traditional manual visual inspection (VI)

Methods

Results

Conclusion