Substrate Defect Detection via Adaptive Histogram Thresholding
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Solution Overview
Problem
Current methods for detecting defects on substrates during semiconductor chip manufacturing are inefficient, as they often require reference images and binary processing, leading to inaccurate defect area identification and increased production time and costs.
Innovation Solution
A method and apparatus that convert captured images to grayscale, calculate specific gray level values for thresholding, and apply the Canny algorithm with optimized low and high thresholds to detect edges, allowing for accurate defect area identification without a reference image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional defect detection methods using reference images and binary processing are used, then defect detection can be performed, but the accuracy of defect area identification is low and production time is increased
Solution Approach 1:
The patent applies parameter changes by using histogram analysis to dynamically determine optimal threshold values for edge detection. Instead of using fixed binary processing, the system calculates gray level values from the image histogram and uses these as adaptive thresholds in the Canny edge detection algorithm, improving both accuracy and processing efficiency
Solution Approach 2:
The patent replaces conventional binary processing methods with a more sophisticated image processing approach based on histogram analysis and Canny edge detection. This substitution of processing mechanisms enables more accurate defect identification while maintaining computational efficiency
2Measurement precision
If conventional binary processing is used for defect detection, then the process is simple, but the accuracy of distinguishing normal and defective areas is insufficient
Solution Approach 1:
The patent performs preliminary action by conducting histogram analysis and calculating optimal threshold values before applying edge detection. This pre-processing step prepares the image data in an optimized state, enabling more accurate defect detection while keeping the overall process manageable
Solution Approach 2:
The patent introduces histogram analysis as an intermediary step between simple binary processing and complex defect detection. This intermediary process extracts meaningful gray level information from the image, which then serves as optimized input for the Canny edge detection algorithm
3Reliability
If reference images are used for defect detection, then defect areas can be identified, but the method requires additional processing steps and time
Solution Approach 1:
The patent applies self-service by enabling the image processing system to automatically determine optimal thresholds through histogram analysis of the input image itself. The system does not require external reference images or manual threshold setting, as it autonomously adapts to the specific characteristics of each image, improving both reliability and productivity
Data Source
AI summary
A method for detecting a defect on a substrate, including receiving a first image, generating a second image, by converting the first image to grayscale levels, calculating a first gray level value, having a maximum number of pixels in the second image, and second and third gray level values, having a number of pixels in the second image equal to a predetermined fraction of the first gray level value, from a histogram of the number of pixels respective to the grayscale levels of the second image, converting the second image into a third image having pixels at a level lower than that of the first gray level value and a fourth image having pixels at a level equal to or higher than the first gray level value, generating fifth and sixth images by detecting edges by applying a Canny algorithm to the third and fourth images, respectively.