Automated Semiconductor Inspection Recipe Generation
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Solution Overview
Problem
Current methods for generating inspection recipes for semiconductor devices are time-consuming and lack effective feedback, requiring multiple iterations and manual management, leading to inefficiencies and inconsistencies across different wafers.
Innovation Solution
A method and apparatus for automated inspection recipe generation that allows offline tuning and refinement using a 'tune map' or 'reference map' database, enabling the selection and classification of dies of interest, and automatic analysis of inspection results to minimize overkill and underkill, without the need for continuous re-inspection of entire wafers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recipe tuning is performed by reviewing individual die images and modifying parameters iteratively, then inspection accuracy can be improved, but the time required for recipe generation increases significantly
Solution Approach 1:
The system implements automated feedback by analyzing inspection results and automatically adjusting recipe parameters based on detected defects and performance metrics, eliminating the need for manual parameter tuning while maintaining high inspection accuracy
Solution Approach 2:
The inspection system performs self-tuning by automatically generating and optimizing inspection recipes based on reference wafer data and defect patterns, allowing the system to service itself without operator intervention for recipe creation and optimization
2Reliability
If the complete wafer is re-inspected multiple times during recipe tuning, then inspection results can be validated, but productivity decreases due to repeated full-wafer inspections
Solution Approach 1:
The system segments the validation process by selecting and inspecting only representative regions or areas of interest on the wafer rather than re-inspecting the entire wafer, maintaining validation reliability while significantly reducing inspection time and increasing productivity
Solution Approach 2:
The system performs partial inspection by focusing on critical areas or representative samples during recipe tuning, which is sufficient for validation purposes without requiring complete wafer re-inspection, thereby improving productivity
3Measurement precision
If recipe parameters are manually modified and saved as new versions, then inspection performance can be optimized, but device complexity increases due to multiple recipe versions and manual management
Solution Approach 1:
The system automatically manages recipe versions and parameter optimizations through self-tuning algorithms, eliminating the need for manual recipe version creation and management, thereby reducing operational complexity while maintaining inspection performance
Solution Approach 2:
The system optimizes inspection performance by dynamically adjusting recipe parameters based on automated analysis of inspection results and defect patterns, achieving performance improvement without the complexity of manual parameter modification and version management
4Measurement precision
If inspection results are reviewed on individual die level, then detection precision can be maintained, but loss of time increases due to manual review of each die
Solution Approach 1:
The system implements automated feedback mechanisms that analyze inspection results across multiple dies simultaneously, providing comprehensive defect detection and classification without requiring manual review of individual dies, thus maintaining precision while reducing time loss
Solution Approach 2:
The system replaces manual mechanical review of individual dies with automated computational analysis that processes multiple die images and defect data simultaneously, maintaining detection precision while dramatically reducing the time required for result review
Data Source
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AI summary
A method, a computer system and an apparatus are disclosed for inspection recipe generation for the automated inspection of semiconductor devices. In order to generate the inspection recipe a reference data set is used. Automatic inspection is carried out with an initial recipe on images of dies of the reference data set (reference wafermap). The detected inspection results from the automatic inspection are classified and the classified inspection results are compared with an expert classification of defects in dies. Overkill and underkill numbers are automatically generated. According to the overkill and underkill numbers the inspection recipe parameters are modified. Automatic inspection is repeated if the detection and/or the classification are below a predefined threshold.