Reticle Defect Detection and Classification via Multi-Resolution Imaging
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Solution Overview
Problem
Current reticle inspection methods face challenges in accurately detecting and classifying defects, particularly in differentiating between real defects and noise or nuisance events, and in determining the significance of pattern errors that may affect semiconductor wafer production, due to the complexity of modern IC designs and the introduction of reticle enhancement techniques.
Innovation Solution
A method that acquires images of reticles at different conditions, such as high and low resolutions, during inspection, allowing for simultaneous defect detection and classification. This involves using high-resolution images for detailed defect identification and low-resolution images to simulate how the reticle will print on a wafer, enabling the classification of defects based on their potential impact on wafer patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution imaging is used for defect detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The inspection process is segmented into multiple imaging steps with different resolutions. High-resolution images are acquired for initial defect detection, then low-resolution images are acquired for classification. This segmentation allows each imaging step to be optimized for its specific purpose, maintaining high detection accuracy while reducing overall system complexity through specialized subsystems for each resolution level.
2Reliability
If multiple imaging conditions are used for defect classification, then reliability is improved, but loss of time increases
Solution Approach 1:
High-resolution images are acquired first to detect all potential defects. Then low-resolution images are acquired to classify defects before final analysis. This preliminary action sequence allows the system to focus processing resources on classified defects only, improving reliability while reducing total inspection time by avoiding unnecessary high-resolution imaging of all areas.
Solution Approach 2:
The system creates multiple image copies of the reticle under different imaging conditions (high-resolution and low-resolution). These copies are then processed independently for defect detection and classification respectively. This copying approach allows parallel processing of different image sets, improving classification reliability without proportionally increasing inspection time.
3Productivity
If operator-dependent review is reduced, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The system performs automated defect classification using low-resolution images and image processing algorithms, eliminating the need for operator-dependent review. The classification algorithm automatically distinguishes between real defects and nuisance events based on image characteristics, maintaining high measurement precision while significantly improving productivity through automated processing.
Solution Approach 2:
The manual operator review process is replaced with an automated image processing and classification system. The mechanical/human analysis step is substituted with computational algorithms that analyze low-resolution images to classify defects. This substitution maintains classification accuracy while eliminating bottlenecks associated with manual review, thereby improving productivity.
Data Source
AI summary
Methods for detecting and classifying defects on a reticle are provided. One method includes acquiring images of the reticle at first and second conditions during inspection of the reticle. The first condition is different than the second condition. The method also includes detecting the defects on the reticle using one or more of the images acquired at the first condition. In addition, the method includes classifying an importance of the defects detected on the reticle using one or more of the images acquired at the second condition. The detecting and classifying steps are performed substantially simultaneously during the inspection.