Pixel Defect Map Adjustment for Imager Devices
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Solution Overview
Problem
Existing methods for detecting and correcting pixel defects in solid state imager devices, especially cluster defects, are limited by the need for elaborate defect identification and storage during manufacturing and struggle to distinguish between true defects and image elements during image acquisition, leading to potential misclassification and image degradation.
Innovation Solution
A method and system for on-the-fly adjustment of a pixel defect map that analyzes multiple frames to accurately locate and classify true pixel defects, including cluster defects, without requiring elaborate defect identification during manufacturing, using a process that updates a defect map during image processing and corrects defective pixels using neighboring signal values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If off-line testing is performed at fabrication time to identify defective pixels, then defective pixel locations can be stored in non-volatile memory for correction, but the number of correctable defects is limited by memory size and requires separate manufacturing steps
Solution Approach 1:
The patent performs preliminary action by creating a defect map during the fabrication process using test patterns, but instead of storing in non-volatile memory, it stores in volatile memory that is reset between frames. This allows the defect map to be updated dynamically during image acquisition without requiring permanent storage of defect locations.
Solution Approach 2:
The system performs self-service by automatically detecting and updating defect locations during normal image acquisition through the flying pixel test. The defect map is continuously refined using actual image data without requiring separate manufacturing steps or external intervention, allowing the system to correct defects as they are detected during operation.
2Ease of manufacture
If on-the-fly cluster correction methods are used to detect true defects during image acquisition, then defect detection can be performed without separate manufacturing steps, but the system struggles to distinguish between true defects and small image elements leading to misclassification
Solution Approach 1:
The patent implements feedback by continuously updating the defect map using results from the flying pixel test during image acquisition. The system compares detected defect patterns against the current defect map and adjusts classifications accordingly. This iterative feedback process allows the system to distinguish true defects from image elements by observing consistency across multiple frames and comparing against established defect patterns.
Solution Approach 2:
The system performs preliminary action by creating an initial defect map during fabrication using test patterns, then refines this map during operation. The preliminary defect map provides a baseline that helps distinguish true defects from false positives during on-the-fly detection, as true defects should consistently appear in the same locations across different image frames.
3Measurement precision
If multiple frames are analyzed to accurately locate true pixel defects, then defect classification accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary action by creating a defect map during fabrication that is then used as a reference during operation. This preliminary map reduces the computational burden during image acquisition, as the system only needs to verify and update existing defect locations rather than perform complete analysis from scratch for each frame.
Solution Approach 2:
The system applies partial action by analyzing only the changes between frames rather than processing all pixel data from all frames completely. The flying pixel test selectively samples pixels to update the defect map, and the system uses only the necessary information to refine defect classifications, reducing computational complexity while maintaining accuracy.
Data Source
AI summary
A method, apparatus and system that allows for the identification of defective pixels, for example, defective pixel clusters, in an imager device. The method, apparatus and system determine, during use of the imager device, that a pixel defect, e.g., cluster defect, exists and accurately maps the location of the defective pixel. By analyzing more than one frame of an image, the method increases the accuracy of the defect mapping, which is used to improve the quality of the resulting image data.