Newton Ring Mura Detection via Spatial Filtering and Frequency Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual visual inspection of Newton ring mura defects in flat panel displays is inconsistent, time-consuming, and limited by inspector skill and fatigue, making it difficult to detect and classify these circular, color-based non-uniformities across mass-produced displays.
Innovation Solution
A system that captures high-resolution images of displays under uniform grey scale illumination, applies non-uniformity normalization, and uses frequency-based detection with border removal and spatial filtering to identify Newton ring mura, followed by post-processing to remove false positives and refine defect characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection is used to detect Newton ring mura defects, then inspector expertise can identify color-based non-uniformities, but the process is inconsistent, time-consuming, and limited by inspector fatigue
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated optical detection system. The system uses an imaging device to capture display images and automatically processes them through non-uniformity normalization and frequency-based detection algorithms, eliminating dependence on human inspectors and their subjective judgment while maintaining high detection accuracy for Newton ring mura defects
Solution Approach 2:
The patent creates a digital copy of the display through imaging that can be analyzed without affecting the original display. The captured image is processed through normalization and frequency analysis to detect defects, allowing repeated analysis without inspector fatigue and enabling parallel processing of multiple displays simultaneously
2Reliability
If manual visual inspection is performed by skilled inspectors, then Newton ring mura can be identified, but the inspection process is heavily dependent on inspector skills and expertise
Solution Approach 1:
The patent replaces the complex human expert system with an automated optical detection system that applies consistent mathematical algorithms. The non-uniformity normalization and frequency-based detection processes eliminate variability introduced by different inspectors' skills, providing reliable and repeatable detection results across all inspections
Solution Approach 2:
The patent transforms the inspection approach by changing from subjective visual parameter assessment to objective mathematical parameter analysis. By converting image data into frequency domain representations and applying normalized detection thresholds, the system achieves consistent results independent of inspector expertise while maintaining reliability
3Measurement precision
If multiple inspectors are trained to maintain inspection quality, then detection accuracy improves, but the cost and time for training and coordination increases
Solution Approach 1:
The patent replaces multiple human inspectors with a single automated system that can process multiple displays simultaneously. The automated imaging and processing system eliminates the need for training multiple inspectors while maintaining high detection accuracy and enabling parallel processing that increases overall inspection throughput
Solution Approach 2:
The automated system enables continuous inspection operation without the interruptions caused by inspector shifts, breaks, or coordination requirements. The system can continuously capture, process, and analyze display images without loss of detection quality, maximizing productivity while maintaining consistent accuracy
Data Source
AI summary
A system for detecting newton ring mura on a display includes sensing an image of the display with an image capture device and determining a border boundary of an illuminated portion of the display. The image is spatially filtered as defined by the border boundary using a filter that reduces sensor noise and a grid pattern of the display. The spatially filtered image is processed to determine if a region proximate a pixel location is a potential newton ring mura defect and characterizing the potential newton ring mura defects to remove at least one of the potential newton ring mura defects.