Substrate Analysis System Multi-Wavelength Defect Classification
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Solution Overview
Problem
Existing substrate analysis systems lack efficient methods to accurately determine defect types and their causing factors on substrate surfaces, particularly in multi-layered films, due to limitations in imaging and gray value analysis.
Innovation Solution
A substrate analysis system incorporating an imaging unit, a gray value acquisition unit, and a defect type determination unit that estimates defect ranges and types based on imaging results and gray values across multiple wavelengths, enabling precise defect identification and factor estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging methods are used to analyze substrate defects, then the analysis process is simple, but the defect type determination precision is insufficient
Solution Approach 1:
The patent introduces multi-wavelength imaging to add a spectral dimension to the traditional single-wavelength imaging. By capturing images at multiple wavelengths and analyzing gray value variations across these wavelengths, the system can distinguish different defect types more accurately. This dimensional expansion transforms the analysis from spatial-only to spatial-spectral combined, resolving the contradiction between precision improvement and system complexity.
Solution Approach 2:
The patent changes the imaging parameter from single wavelength to multiple wavelengths. By varying the wavelength parameter and measuring gray value responses at each wavelength, the system creates a spectral signature for different defect types. This parameter change enables more precise defect classification while managing system complexity through systematic data processing methods.
2Measurement precision
If multiple wavelengths are used for defect analysis, then the defect type determination precision improves, but the analysis time increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the relationship between gray values at different wavelengths and defect types. The system captures multiple wavelength images and processes this data in advance to create a comprehensive defect characterization. This preliminary multi-wavelength analysis framework is built once and can be applied systematically, reducing the time penalty of multi-wavelength imaging through efficient data reuse and pattern recognition.
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 system enables high-precision defect type determination and identification of defect-causing factors, improving the analysis of substrate processing states and reducing processing time by efficiently analyzing defects across multiple regions and wavelengths.
Implementation Method 1
an imaging unit (11) configured to image a substrate surface
Implementation Method 2
a gray value acquisition unit configured to acquire a gray value in the defect range when light is radiated to the substrate surface
Data Source
AI summary
An analyzing device 10 includes an imaging unit 11 configured to image a substrate surface; a defect range estimation unit 131 configured to estimate a defect range, which is a range in which a defect exists on the substrate surface, based on an imaging result of the imaging unit 11; a gray value acquisition unit 132 configured to acquire multiple gray values in the defect range when light is radiated to the substrate surface; and a defect type determination unit 133 configured to determine a defect type in the defect range based on the gray value.