Semiconductor Wafer Color Difference Detection Using Grayscale Template Matching
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Solution Overview
Problem
Existing color difference detection technologies for semiconductor wafers face challenges due to large grayscale value differences between die images, leading to matching errors and inaccurate defect detection when using a single standard reference image.
Innovation Solution
Creating multiple template images based on a reference object with varying average grayscale values, allowing for the selection of a template image closest to the average grayscale value of the die image to be detected, thereby improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single standard reference die image is used for all die images, then the detection process is simple and fast, but matching errors and misalignment occur when grayscale value differences between die images are large
Solution Approach 1:
The patent divides the single standard reference die image into multiple template die images, each corresponding to a specific grayscale value range. By segmenting the reference images according to grayscale characteristics, the system can select the most appropriate template for each die image to be detected, thereby improving matching accuracy while managing complexity through organized segmentation.
Solution Approach 2:
The patent changes the parameter of template image selection based on the grayscale value of the die image to be detected. By dynamically selecting templates according to grayscale parameter matching, the system adapts to varying grayscale conditions without requiring a completely complex detection system architecture.
2Adaptability or versatility
If the reference threshold setting range is expanded to compensate for grayscale value differences, then more die images can be matched, but defects with smaller contrast are missed or matching accuracy decreases
Solution Approach 1:
The patent applies local quality by creating different template images optimized for specific grayscale value ranges rather than using a single universal template. Each template has localized characteristics suited to its grayscale range, allowing the system to maintain high detection precision within each range while covering a broad overall grayscale spectrum through multiple specialized templates.
Data Source
AI summary
The present disclosure discloses a detection method and a detection system. The detection method comprises: creating a plurality of template images based on a reference object, wherein the reference object includes a plurality of units, and the plurality of template images are unit images with different average grayscale values; calculating a first average grayscale value of a unit image to be detected; selecting a first template image from the plurality of template images based on the first average grayscale value, wherein a difference between an average grayscale value of the first template image and the first average grayscale value is smallest; performing color difference detection on the unit image to be detected based on the first template image. The present disclosure can select a template image whose grayscale value is similar to the grayscale value of a unit image to be detected to detect the unit, so that the difference between the unit image to be detected and the template image is smallest, thereby reducing the frequency of false detection and missed detection, and ultimately increasing the detection effect of the color difference detection.