Semiconductor Substrate Defect Detection via RGB Alignment Comparison
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Solution Overview
Problem
Current semiconductor defect detection methods are time-consuming and prone to errors due to misjudgment of defect positions and noise, especially with the increasing complexity and miniaturization of semiconductor components, leading to inconsistent defect detection results.
Innovation Solution
A method involving alignment, affine transformation, color difference comparison, and noise filtering steps to convert color images into red, green, and blue images, followed by erosion and dilation to accurately identify defects and eliminate noise, using alignment marks and standard images for comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual defect detection is used, then detection accuracy can be maintained, but detection time increases significantly and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical inspection system that captures color images of semiconductor substrates and uses computer-based processing to detect defects. This substitution maintains high detection accuracy while dramatically improving detection speed and productivity.
Solution Approach 2:
The system creates a digital copy (color image) of the semiconductor substrate and performs defect detection on this copy rather than requiring direct manual inspection. This allows multiple analyses to be performed on the same copy, improving both efficiency and accuracy.
2Productivity
If automated defect detection is implemented, then productivity improves, but measurement precision deteriorates due to misjudgment of defect positions and noise
Solution Approach 1:
The patent segments the color image into three separate color channels (red, green, blue) and processes each channel independently. This segmentation allows the system to analyze specific color characteristics and distinguish defects from noise more effectively, improving measurement precision while maintaining automated high-speed detection.
Solution Approach 2:
The system utilizes color information by capturing images in multiple color channels and analyzing color differences between the captured image and a reference image. This color-based analysis enables the automated system to accurately distinguish defects from noise, resolving the precision issue while maintaining high productivity.
3Measurement precision
If color image processing is used, then detection accuracy improves through color difference comparison, but device complexity increases
Solution Approach 1:
The patent divides the color image processing into three separate single-color channel processes (red, green, blue). This segmentation simplifies the overall processing complexity by breaking down the complex color analysis into three simpler, independent processing streams that can be handled more efficiently.
4Measurement precision
If noise filtering is applied, then measurement precision improves by eliminating false positives, but processing time increases
Solution Approach 1:
The patent performs noise filtering through erosion and dilation operations as preliminary steps before final defect identification. By preparing and filtering the image data in advance, the system reduces the need for repeated processing and reanalysis, ultimately saving time while improving the accuracy of defect identification.
Data Source
AI summary
The invention relates to a method for detecting defects of a semiconductor substrate, which comprises the following steps: providing a substrate, wherein the substrate comprises at least one alignment mark; capturing the substrate to obtain a color image, wherein the color image comprises at least one first alignment mark, inputting the color image into a system, and respectively retaining a R value, a G value and a B value of the color image, so as to respectively convert the color image into a red image, a green image and a blue image, and carry out a color difference comparison step, wherein the red image, the green image and the blue image are respectively compared with a standard red image, a standard green image and a standard blue image, and finding the color difference regions on the red image, the green image and the blue image.