AI Optical Compensation for Display Panel Luminance
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Solution Overview
Problem
Existing self-luminous display devices, such as organic light-emitting display devices, face issues with varying optical characteristics across panels, leading to deteriorated image quality due to insufficient or time-consuming optical compensation.
Innovation Solution
An optical compensation system and method utilizing artificial intelligence, including a measuring device and an AI-based controller that predicts and generates data voltage optimized for each display panel's optical characteristics using a neural network, enabling fast and accurate compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional optical compensation methods are used, then optical compensation can be performed, but the compensation processing time is very long and compensation performance is insufficient
Solution Approach 1:
The patent creates a virtual model (digital twin) of the display panel that replicates its optical characteristics and aging behavior. This virtual model allows compensation calculations to be performed on copied data rather than requiring extensive physical measurements and iterations, dramatically reducing processing time while maintaining compensation accuracy.
Solution Approach 2:
The system performs preliminary characterization of the display panel's optical characteristics during manufacturing and creates a baseline virtual model. Compensation parameters are pre-calculated and stored in lookup tables based on this preliminary analysis, enabling fast real-time compensation without extensive processing during operation.
2Measurement precision
If traditional optical compensation methods are used, then compensation can be attempted, but the compensation accuracy is insufficient due to varying optical characteristics across panels
Solution Approach 1:
The patent divides the display panel into multiple measurement regions and characterizes optical characteristics locally for each region. The virtual model stores spatially-resolved optical parameters that capture local variations across the panel, enabling region-specific compensation that addresses local quality differences rather than applying uniform compensation.
Solution Approach 2:
By creating a detailed virtual model that copies the specific optical characteristics of each individual panel, the system enables accurate prediction and compensation of panel-specific variations. The virtual model preserves the unique optical fingerprint of each panel, allowing precise tailoring of compensation parameters to match actual panel behavior.
3Manufacturing precision
If comprehensive optical measurements are performed for each panel, then accurate compensation data can be obtained, but the measurement and processing time increases significantly
Solution Approach 1:
Essential optical characteristics are measured and stored in the virtual model during manufacturing as preliminary data. This preliminary characterization captures the dominant optical behavior patterns, enabling accurate compensation without requiring exhaustive measurements during quality control or operation.
Solution Approach 2:
The virtual model creates a simplified yet accurate representation (copy) of the panel's optical characteristics, capturing essential behavior patterns without requiring complete detailed measurements. This copied model enables fast compensation calculations that would otherwise require extensive measurement and processing.
Data Source
AI summary
An optical compensation system based on artificial intelligence according to embodiments of the present disclosure may include a measuring device configured to measure optical characteristics of a display panel, and output measurement result data of the optical characteristics, and an artificial intelligence-based optical compensation controller configured to predict and generate optical compensation result data corresponding to the measurement result data of the optical characteristics based on an artificial intelligence neural network using previous optical compensation result data for at least one other display panel, and store the predicted and generated optical compensation result data in a memory corresponding to the display panel.