Automated Defect Analysis for OLED Panels via Coordinate Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the manufacturing of display panels, especially organic light emitting diode (OLED) panels, defects are difficult to trace due to the complexity of the integrated process, often requiring manual data sorting based on experience, which is inefficient and unreliable.
Innovation Solution
A computer-implemented method for defect analysis that involves obtaining and combining defect point coordinates into a composite image, performing clustering analysis to classify defect points, determining contours and mask areas, generating feature vectors, and identifying potential devices causing defects through comparative analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data sorting based on experience is used for defect analysis, then defect tracing can be performed, but the process is inefficient and unreliable
Solution Approach 1:
The patent replaces manual mechanical data sorting with an automated computer-implemented system that uses coordinate combination, clustering analysis, and mask area generation to identify defect sources. The system automatically processes defect point coordinates from multiple substrates, performs hierarchical clustering, generates contours and mask areas, and identifies potential defective devices without human intervention, thereby improving both reliability and efficiency
Solution Approach 2:
The system enables self-service defect analysis by automatically combining coordinate data from multiple substrates, performing clustering analysis to group defect points, generating mask areas that highlight defect concentrations, and identifying potential defective devices. The entire process is self-contained and requires no external manual sorting or interpretation, allowing the system to autonomously trace defect sources
2Reliability
If manual data sorting is used to trace defects in OLED manufacturing, then defect analysis can be performed, but the process is complex and time-consuming
Solution Approach 1:
The patent replaces time-consuming manual data sorting with automated computer processing that combines defect point coordinates from multiple substrates, performs rapid clustering analysis using algorithms such as hierarchical clustering or density-based clustering, generates mask areas through contour detection, and identifies defective devices instantaneously. This automated approach maintains high tracing accuracy while reducing analysis time from hours or days to minutes
Solution Approach 2:
The system performs preliminary actions by pre-processing defect point coordinates into a unified coordinate system, pre-grouping defect points through clustering analysis, and pre-generating mask areas before final defect source identification. These preliminary processing steps organize the data structure in advance, enabling rapid and accurate defect tracing without manual intervention during the actual analysis phase
Data Source
AI summary
A computer-implemented method for defect analysis is provided. The computer-implemented method includes obtaining a plurality of sets of defect point coordinates, a respective set of the plurality of sets of detect point coordinates including coordinates of defect points in a respective substrate of a plurality of substrates, the coordinates of defect points in the respective substrate being coordinates in an image coordinate system; combining the plurality of sets of defect point coordinates according to the image coordinate system into a composite set of coordinates to generate a composite image; and performing a clustering analysis to classify defect points in the composite set in the composite image into a plurality of clusters.