LCD Inspection Using Polarized Backlight and Multi-Mode Illumination
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Solution Overview
Problem
Existing LCD inspection methods rely on human observation, leading to inaccurate and time-consuming defect detection, particularly in distinguishing between defects and fine dust on the panel surfaces, resulting in incorrect classification of non-defective products and increased manufacturing costs.
Innovation Solution
An automated LCD inspection apparatus and method utilizing a worktable, probe units, a backlight, an imaging unit, polarizing plates, illumination units, and an image processor to photograph and analyze images of the LCD panel, distinguishing between defects and foreign matter through comparative image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated image processing is implemented to accurately distinguish defects from fine dust, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The inspection system is divided into multiple functional modules: illumination units for different lighting conditions, imaging units for capturing images, and an image processor for analyzing the captured images. This segmentation allows each module to be optimized independently while working together to achieve accurate defect detection that distinguishes defects from fine dust particles.
Solution Approach 2:
The image processor acts as an intermediary between the imaging units and the defect identification system. It receives images from multiple imaging units, processes them through algorithms that compare images taken under different illumination conditions, and automatically identifies whether detected points are defects or fine dust, thereby improving measurement precision without requiring direct human intervention.
2Measurement precision
If multiple imaging units with different illumination are used to photograph LCD panel, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Different illumination units provide locally optimized lighting conditions for detecting different types of anomalies. The first illumination unit provides uniform illumination for general inspection, while the second illumination unit provides directional illumination specifically optimized for highlighting fine dust particles on the LCD panel surface, allowing the system to distinguish between defects and dust based on their different optical characteristics.
Solution Approach 2:
The system changes illumination parameters by using multiple imaging units with different illumination conditions. By capturing images under varying lighting parameters (uniform vs. directional illumination) and comparing them, the image processor can identify whether detected points are defects or fine dust particles, thereby improving measurement precision through parameter variation.
3Productivity
If automated inspection system is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by capturing multiple images under different illumination conditions before making a defect determination. The image processor automatically processes these pre-captured images, compares them, and identifies defects or fine dust particles, thereby improving productivity through automation while managing complexity through systematic preprocessing of inspection data.
Solution Approach 2:
The image processor implements feedback by automatically analyzing images from multiple imaging units, comparing the captured images, and providing definitive identification of defects versus fine dust particles. This automated feedback loop eliminates manual inspection, improves productivity, and manages complexity through algorithmic processing that systematically evaluates the captured image data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables accurate and automated defect detection, reducing human error and manufacturing costs by effectively differentiating between defects and foreign matter on the LCD panel surfaces, thereby improving yield and inspection efficiency.
Implementation Method 1
a first polarizing plate which is arranged between the imaging unit and the LCD panel to polarize the light, a second polarizing plate which is arranged between the LCD panel and the backlight unit to polarize the light
Implementation Method 2
a backlight unit which supplies light to the LCD panel
Implementation Method 3
an illumination unit which emits illumination light to surfaces of the LCD panel supported by the worktable
Data Source
AI summary
The liquid crystal display (LCD) inspection apparatus for inspecting an LCD panel includes a worktable which supports the LCD panel to be seated on a front side of the worktable, probe units which are electrically connected to the LCD panel, a backlight unit which supplies light to the LCD panel, an imaging unit which photographs an image of the LCD panel supported by the worktable, a first polarizing plate which is arranged between the imaging unit and the LCD panel to polarize the light, a second polarizing plate which is arranged between the LCD panel and the backlight unit to polarize the light, an illumination unit which emits illumination light to surfaces of the LCD panel, and an image processor which receives the image photographed by the imaging unit, and extracts defect information from the received image.