Wafer Inspection Image Contrast Calibration for Focus Stability
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Solution Overview
Problem
Current inspection systems for semiconductor wafers are inadequate in detecting focus changes and image contrast variations, leading to inefficiencies in yield management and high-yield production, as they lack sensitivity and require time-consuming manual calibration.
Innovation Solution
A method and system that utilize a processor to extract and align image frames, determine image contrast ratios, and adjust the stage position based on these ratios to maintain focus and detect defects, thereby improving focus accuracy and reducing manual calibration needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is used to adjust focus, then focus accuracy can be improved, but productivity decreases due to time-consuming calibration steps
Solution Approach 1:
The inspection system performs automatic focus calibration by comparing image contrast of runtime images to golden die images, eliminating the need for manual operator intervention. The system self-adjusts focus parameters based on contrast ratio calculations, enabling continuous operation without productivity loss.
Solution Approach 2:
Golden die images are captured and stored during setup as reference standards for focus calibration. These pre-established reference images enable rapid automatic comparison and calibration during runtime, eliminating the need for time-consuming manual calibration procedures while maintaining focus accuracy.
2Ease of operation
If autofocus systems are used to detect focus changes, then ease of operation improves, but measurement precision deteriorates due to insufficient sensitivity
Solution Approach 1:
The system uses image contrast ratio as a sensitive parameter to detect focus changes. By calculating the ratio between runtime image contrast and golden die image contrast, the system achieves high sensitivity in detecting even minor focus variations, far exceeding traditional autofocus capabilities.
Solution Approach 2:
The patent replaces traditional mechanical autofocus systems with an optical-based image contrast analysis method. Instead of relying on mechanical sensors with limited sensitivity, the system uses optical image processing and contrast ratio calculations to achieve superior focus detection sensitivity.
3Measurement precision
If run-time focus calibration is performed, then measurement precision improves, but loss of time increases due to calibration duration
Solution Approach 1:
The system performs automatic focus calibration by comparing image contrast of runtime images to golden die images, eliminating the need for manual operator intervention. The system self-adjusts focus parameters based on contrast ratio calculations, enabling continuous operation without productivity loss.
Solution Approach 2:
The focus calibration process is integrated into the runtime inspection workflow, allowing focus verification and adjustment to occur continuously during normal operation rather than requiring separate calibration stops. This maintains measurement precision without interrupting production flow.
4Measurement precision
If manual calibration is used, then measurement precision can be maintained, but device complexity increases due to operator involvement
Solution Approach 1:
The inspection system performs automatic focus calibration by comparing image contrast of runtime images to golden die images, eliminating the need for manual operator intervention. The system self-adjusts focus parameters based on contrast ratio calculations, enabling continuous operation without productivity loss.
Data Source
AI summary
Wafer-to-wafer and within-wafer image contrast variations can be identified and mitigated by extracting an image frame during recipe setup and then during runtime at the same location. Image contrast is determined for the two image frames. A ratio of the contrast for the two image frames can be used to determine contrast variations and focus variation.


