Transparent Substrate Defect Inspection via Wavefront Reconstruction
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Solution Overview
Problem
Current defect detection technologies for transparent substrates, such as glass, are inadequate for rapid and effective detection, particularly in miniaturized manufacturing processes, as they often rely on manual or semi-mechanical methods and machine vision, which struggle with focusing on transparent substrates, leading to time-consuming and incomplete defect identification.
Innovation Solution
A wide field digital holography system utilizing up-sampling techniques and spherical reference light waves to enhance image resolution, combined with a defect diffraction module and machine learning algorithms for quantitative wavefront measurement and defect classification, allowing for three-dimensional defect analysis and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine vision is used to detect defects on transparent substrate, then defect detection can be automated, but the camera cannot effectively focus on transparent substrate leading to loss of focus and incomplete defect identification
Solution Approach 1:
The patent introduces a reference substrate with known defect patterns as an intermediary element. This reference substrate serves as a mediator between the camera system and the transparent substrate being inspected, providing a stable focusing target that compensates for the camera's inability to focus directly on transparent materials. The reference substrate's defect patterns enable automated detection while maintaining focus accuracy.
2Measurement precision
If complicated pre-signal processing and classification algorithms are applied to improve defect detection accuracy, then detection precision can be improved, but detection time increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-processing the transparent substrate to attach reference markers or patterns before inspection. This preliminary step creates a standardized reference framework that simplifies subsequent defect detection algorithms, reducing the computational complexity and time required for processing while maintaining high detection accuracy.
Solution Approach 2:
The patent extracts and separates the reference pattern information from the defect detection process. By isolating the reference substrate patterns as a distinct element, the system can process reference information and defect information separately, reducing the overall computational burden and detection time while maintaining precision.
3Productivity
If two-dimensional imaging is used through machine vision, then detection can be performed, but image intensity calculation is complex and three-dimensional imaging is time-consuming
Solution Approach 1:
The patent transitions from two-dimensional intensity-based imaging to three-dimensional optical path difference imaging. By utilizing optical interference patterns and phase information, the system captures depth and topography data in addition to intensity information, enabling simpler intensity calculations while providing comprehensive defect characterization without excessive time consumption.
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
This approach enables rapid and accurate detection of defects like bubbles, dust, and scratches on transparent substrates, improving detection efficiency and resolution while reducing detection time, thereby enhancing production yield and reducing resource waste.
Implementation Method 1
a wavefront reconstruction unit to obtain defect complex optical field of a transparent substrate
Implementation Method 2
a defect diffraction module to determine an effective diffraction range of the defect complex light field
Data Source
AI summary
A method for defect inspection of a transparent substrate comprises utilizing a wavefront reconstruction unit to obtain complex defect diffraction wavefront of a transparent substrate; using a complex defect diffraction module to confirm the effective diffraction distance of the complex defect diffraction wavefront; utilizing a defect detection module to detect position of the defect of the transparent substrate; using a defect classification module to perform extraction, analysis and classification of diffraction characteristics and utilizing a machine learning algorithm or a deep learning algorithm to automatically identify the defects.