OLED Pixel Inspection Using Generative Fault Mode Labeling

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Solution Overview

Problem

Existing technologies have not effectively addressed the determination of seepage defects in pixel regions of organic EL display panels, particularly the identification of the seepage defects in the determination of the fault mode of the determination of the determination of the determination of the determination of the determination of the determination of the determination of the determination of the determination of the seepage defects in the pixel regions of the organic EL display panels, particularly the seepage defects in the pixel regions of the organic EL display panels, which are not taken into consideration of the seepage defects in the pixel region of the organic EL display panel, which are not taken into consideration as a type of fault in existing image classification methods.

Innovation Solution

An inspection method using a generative model, such as a Pix2Pix neural network, to automatically classify seepage defects in pixel regions of organic EL display panels by converting anomalous portions into color-coded fault modes, and determining the size of these defects to confirm their nature.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual inspection by operators is used to determine fault modes and measure seepage region sizes, then flexibility in handling various defect types is maintained, but judgment consistency deteriorates due to differences among operators and fluctuations over time

Engineering Contradiction:

Improvehandling various defect typesVSAvoidjudgment consistency

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent replaces the manual mechanical inspection process with an automated image processing system using background subtraction methods. The system automatically generates anomalous portion images, extracts feature quantities, and determines fault modes without human intervention, thereby eliminating variability in operator judgment while maintaining the ability to handle multiple defect types through algorithmic classification

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The inspection system performs self-service by automatically determining fault modes and measuring seepage region sizes without requiring operator input. The system uses automated image processing, feature extraction, and classification algorithms to complete the entire inspection process independently, ensuring consistent results across different inspections and operators

Inventive Principle:

Principle #25Self-service

2Productivity

If automated image classification methods are used to classify defects, then productivity is improved, but the ability to detect seepage defects deteriorates because existing methods do not consider seepage defects as a fault type

Engineering Contradiction:

Improveinspection automationVSAvoidseepage defect detection accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent changes the classification parameters and feature extraction methods to specifically accommodate seepage defects. By modifying the image processing parameters, feature quantity selection, and classification criteria, the system enables automated detection of seepage defects while maintaining high productivity through algorithmic processing rather than manual inspection

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If manual inspection is used to ensure accurate fault mode determination, then measurement precision is maintained, but loss of time increases due to operator involvement in each determination

Engineering Contradiction:

Improvefault mode determination accuracyVSAvoidinspection time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements preliminary action by pre-defining fault mode classification criteria, feature quantity thresholds, and decision rules in the automated system. The background subtraction parameters, feature extraction methods, and classification algorithms are predetermined and configured before inspection, enabling rapid automated determination without requiring real-time operator analysis while maintaining precision through systematic evaluation

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250363610A1Inspection method, inspection device, and recording medium

Publication Date: 2025.11.27 MAGNOLIA BLUE CORP

AI summary

An inspection method performed by a computer for inspecting a display panel includes: obtaining an anomalous portion image that includes an anomalous portion of a pixel region of the display panel, the anomalous portion being acquired by performing image processing using a background subtraction method on an inspection image of the pixel region; generating, using a trained generative model, a label image from the anomalous portion image by converting a region indicating the anomalous portion into a region of a color corresponding to a fault mode of the anomalous portion; and determining, based on the color of the region in the label image, whether the fault mode of the anomalous portion has a possibility of being a seepage defect in which light is not emitted due to deterioration of a functional layer in the pixel region. The fault mode includes the seepage defect and a dark-dot defect.