Semiconductor Image Registration for Distortion-Aware Defect Detection
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Solution Overview
Problem
Conventional defect detection methods in semiconductor samples using particle beam inspection systems often result in false-positive defects due to distortions such as rotation and non-linearities, which can lead to unreliable results.
Innovation Solution
A method that divides sample and reference images into regions, corrects lateral offsets, and compares pixel-by-pixel within each region pair to detect defects, effectively addressing distortions and reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional defect detection methods are used to examine semiconductor samples, then measurement coverage and throughput are improved, but false-positive defect detections increase due to image distortions
Solution Approach 1:
The patent divides the sample image into multiple regions and processes each region separately with distortion correction. This segmentation allows the system to maintain high throughput by processing multiple regions in parallel while improving reliability by applying accurate distortion correction to each region individually, thereby reducing false-positive defect detections.
2Measurement precision
If image distortion correction is applied to the entire sample image, then defect detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent divides the sample image into multiple regions and applies distortion correction to each region separately. This segmentation reduces the computational complexity and processing time compared to correcting the entire large image at once, while still maintaining high defect detection accuracy through localized correction.
Solution Approach 2:
The patent applies distortion correction parameters specifically tailored to each local region of the sample image rather than using a single global correction. This local quality approach improves measurement precision by accounting for regional variations in distortion while keeping processing efficient by limiting the correction scope to manageable regions.
3Reliability
If the sample image is divided into smaller regions for processing, then false-positive detections are reduced through better local registration, but the overall processing complexity increases
Solution Approach 1:
The patent divides the sample image into multiple regions and processes each region separately with distortion correction. This segmentation improves reliability by enabling accurate local registration that reduces false-positive detections. The processing complexity is managed by using systematic approaches for region division and parallel processing capabilities.
Data Source
AI summary
A method for defect detection in a sample, such as in a semiconductor sample, includes the following steps: providing a reference image of the sample; providing a sample image generated via a particle beam inspection system, wherein the sample image comprises a rotation with respect to the reference image; dividing the sample image into sample image regions; dividing the reference image into reference image regions, wherein each sample image region is assigned one reference image region to form an image region pair; identifying in each image region pair a structure that is present both in the sample image region and also in the associated reference image region of the image region pair; registering the sample image regions by correcting a lateral offset of the identified structure in each sample image region on the basis of the location of the identified structure in the respectively associated reference image region, as a result of which corrected sample image regions are formed; and comparing each corrected sample image region pixel by pixel with the respectively associated reference image region for defect detection.