Machine vision-based gray relational theory applied to IC marking inspection

Binyan Jiang, Szu-Lang Tasi, Chien-Chih Wang

IEEE Transactions on Semiconductor Manufacturing · 2002 · 107 citations · 9 references

Concepts

TL;DR

IC marking errors continue to pose a challenge in semiconductor manufacturing. This study aims to identify IC markings using gray relational analysis. The method rotates and segments IC images, applies thresholding and thinning to extract features, and then uses gray relational analysis to compute relational grades for marking identification. The approach achieves a 97.5 % identification rate and offers advantages such as low data requirements, distribution‑agnostic operation, and independence from factor independence, making it a practical solution for IC marking inspection.

Abstract

In the semiconductor industry, IC marking error remains a problem. The objective of this study is to identify IC marking using gray relational analysis. The gray theorem determines the gray relational grades of all of the selected factors by choosing the highest gray relational grade, even under incomplete information circumstances. In an IC marking identification procedure, an image is rotated and segmented first. Second, thresholding and thinning operations are applied to reduce the calculation complexity and extract features from the segmented image. Finally, the gray relational analysis method is applied to inspect the IC markings. The identification rate reaches 97.5%. As compared to traditional methods, there are three advantages in gray relational analysis: 1) No large amount of data is needed; 2) No specific statistical data distribution is required; and 3) There is no requirement for the independency of the factors to be considered. It is an easy and practical method in the field of IC marking inspection.

References

9