Iterative Unevenness Correction for Display Panel Yield
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Solution Overview
Problem
Existing methods for generating unevenness correction data for display panels, such as those described in Patent Document 1, do not completely eliminate luminance or color unevenness, leading to a high rate of defective products and increased costs due to residual defects.
Innovation Solution
An iterative process involving multiple image captures and data generations, where iteration data is used to correct display panel images, with ending conditions such as white noise detection, bright line/bright spot detection, or reaching a predetermined number of captures, to generate effective unevenness correction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correction data is generated by capturing a test pattern image a plurality of times for each tone value, then the influence of optical shot noise is suppressed and highly accurate correction data is obtained, but unevenness is not eliminated completely and some unevenness remains
Solution Approach 1:
The patent segments the correction process into multiple iterative stages. Instead of generating correction data in a single step, the system performs repeated corrections where each iteration refines the correction data further. The correction data generation is divided into: initial correction data generation, application to reduce unevenness, evaluation of residual unevenness, and regeneration of correction data based on remaining unevenness patterns. This segmentation allows the system to progressively eliminate different components of unevenness across multiple passes.
Solution Approach 2:
The patent implements a feedback mechanism where the result of each correction iteration is evaluated and used to generate improved correction data for the next iteration. The system captures images after correction, evaluates the remaining unevenness, and feeds this information back into the correction data generation process. This closed-loop feedback enables continuous refinement of correction data until unevenness is eliminated to the desired level, directly addressing the incomplete elimination problem.
2Reliability
If the repeating step is performed multiple times to further eliminate unevenness, then unevenness elimination improves, but processing time and complexity increase
Solution Approach 1:
The patent makes the correction process dynamic by adaptively determining when to stop iterating based on evaluation results rather than using a fixed number of iterations. The system dynamically adjusts the correction process based on the measured unevenness level after each iteration. When unevenness is eliminated to a predetermined level or no significant improvement is observed, the system automatically terminates the repeating step. This dynamic approach optimizes processing time by avoiding unnecessary iterations while ensuring sufficient correction is achieved.
Solution Approach 2:
The patent changes key parameters during the iterative process, including the correction data itself, the evaluation thresholds, and the termination conditions. The system modifies correction data parameters in each iteration based on observed unevenness patterns. Additionally, the evaluation criteria and termination parameters are adjusted based on the progression of correction effectiveness, allowing the system to efficiently determine when further iterations would be wasteful versus when more iterations are needed to achieve the desired uniformity.
Data Source
AI summary
An unevenness correction data generation method provided for generating unevenness correction data for effectively improving the yield of a display panel. The method includes: a step of capturing an image of a display panel where a predetermined pattern is displayed; a step of generating iteration data for correcting unevenness of the captured image; a step of storing the iteration data in a storage means; a step of capturing an image of the display panel where a pattern in the storage means is displayed; a step of generating iteration data for correcting unevenness of the captured image; a step of storing iteration data in the storage means; a step of judging whether or not an ending condition for ending repetition of the steps is satisfied; and a step of generating the unevenness correction data based on the iteration data stored in the storage means the ending condition is satisfied.