Phase Diversity Inspection Imaging for In-Field Focus Correction
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Solution Overview
Problem
Existing inspection systems face challenges in achieving high-resolution imaging of sub-100 nanometer IC components due to limitations in focus measurement and aberrations, leading to low throughput, inaccurate focus adjustments, and reduced image quality.
Innovation Solution
Implementing a phase diversity analysis to determine focus-related values and maximum likelihood estimates for images within the field of view, allowing for the generation of focus-adjusted images without the need for external focus measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external focus measurements are performed outside the field of view to adjust focus, then focus adjustment can be made, but the measurements do not accurately represent the actual focus conditions within the inspection area, leading to inaccurate focus correction
Solution Approach 1:
The system uses the inspection images themselves to determine focus quality metrics, rather than requiring separate external focus measurements. The focus quality metric is calculated directly from the inspection images by analyzing edge sharpness and contrast, allowing the system to self-evaluate and self-correct focus conditions without external intervention
Solution Approach 2:
The focus measurement function is extracted from the traditional external focus measurement system and integrated directly into the inspection image processing pipeline. By calculating focus quality metrics from the inspection images themselves, the system eliminates the need for separate focus measurement apparatus and procedures
2Manufacturing precision
If multiple images are captured and processed through phase diversity analysis to correct focus deviations, then image quality and sharpness are improved, but computational load and processing time increase
Solution Approach 1:
The system applies phase diversity analysis selectively based on the calculated focus quality metric. When the metric indicates acceptable focus, full phase diversity processing is skipped. Processing is applied only when and where focus deviations are detected, reducing overall computational load while maintaining image quality where needed
Solution Approach 2:
The field of view is divided into multiple regions, and phase diversity analysis is applied independently to each region based on its specific focus quality. This allows parallel processing of different regions and enables the system to focus computational resources only on areas with focus problems rather than processing the entire field uniformly
3Productivity
If focus adjustments are made based on measurements taken outside the field of view, then focus can be adjusted, but throughput is reduced due to inaccurate adjustments requiring re-inspection
Solution Approach 1:
The system calculates a focus quality metric from inspection images and uses this metric as feedback to automatically adjust focus settings. This closed-loop feedback ensures that focus adjustments are based on actual focus conditions within the inspection area, improving both accuracy and throughput by eliminating the need for re-inspection due to incorrect focus adjustments
Solution Approach 2:
The inspection system performs its own focus evaluation and adjustment using the inspection images, eliminating the need for separate external focus measurement steps that reduce throughput. The system self-corrects focus deviations directly from the inspection data, maintaining productivity while improving reliability
Data Source
AI summary
Systems, apparatuses, and methods for improving image quality. In some embodiments, a method may include obtaining a plurality of images of an area of a sample; determining via a phase diversity analysis: ma plurality of focus-related values, wherein each focus-related value of the plurality of focus-related values is associated with each image of the plurality of images; a maximum likelihood estimate of the plurality of images; and generating a focus-corrected image of the area based on the determined plurality of focus-related values and the determined maximum likelihood estimate.