Wafer Defect Detection via Multi-Parameter Image Analysis
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Solution Overview
Problem
Current wafer inspection tools face limitations in defect detection due to light scattered by line edge roughness, which generates speckles indistinguishable from real defects, and existing filtering techniques are ineffective in removing these speckles.
Innovation Solution
A method involving an imager that captures multiple images of a region with varying parameters such as illumination spectrum, polarization, and angle, generating difference images, calculating pixel attributes, and determining defects based on noise attributes, utilizing a high-dimensional analysis to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are captured with varying parameters and high-dimensional analysis is performed, then defect detection accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The patent segments the defect detection process into multiple distinct stages: acquiring images under different illumination/collection conditions, generating difference images by subtraction, calculating pixel attributes from difference images, computing noise attributes from multiple pixel attribute sets, and making defect determination decisions. This segmentation allows each stage to be optimized independently and processed efficiently.
Solution Approach 2:
The patent transitions from analyzing single images to analyzing multiple images captured under varying parameters (illumination angle, collection angle, polarization, spectrum). This adds dimensional information to the analysis, creating a multi-dimensional dataset that enables more accurate defect detection by distinguishing true defects from speckle noise through their different responses across multiple dimensions.
2Measurement precision
If multiple images are captured with varying parameters, then defect detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by first acquiring multiple images under different parameters and generating difference images before the actual defect detection analysis. The pixel attributes and noise attributes are pre-calculated from these difference images, so that when defect determination is needed, the heavy computational work has already been completed, enabling faster real-time defect identification.
Solution Approach 2:
The patent calculates multiple pixel attributes (intensity, standard deviation, skewness, kurtosis) and multiple noise attributes for each pixel, which is more than a single attribute would provide. This excessive calculation of attributes ensures that sufficient information is available to accurately distinguish defects from noise, and the comprehensive attribute set enables efficient defect determination without needing even more complex analysis.
3Ease of manufacture
If conventional filtering techniques are used, then processing is simple, but they fail to remove speckles caused by line edge roughness
Solution Approach 1:
The patent changes the fundamental parameters used for filtering by transitioning from analyzing single images to analyzing multiple images captured under varying illumination and collection conditions. By calculating pixel attributes from difference images and comparing noise attributes across multiple measurements, the system adapts its filtering approach to the specific characteristics of speckle noise caused by line edge roughness, achieving reliable defect detection without sacrificing simplicity.
Solution Approach 2:
The patent introduces difference images as an intermediary between the raw multiple images and the final defect detection. By subtracting reference images from acquired images to create difference images, and then calculating pixel attributes from these difference images, the system creates an intermediate representation that isolates defect signals from speckle noise, enabling reliable filtering that conventional techniques cannot achieve.
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 significantly enhances defect detection accuracy by considering more information from the interaction of the electromagnetic field, achieving an improvement of at least seventy percent.
Implementation Method 1
detection are frequently limited by light scattered by the roughness of the patterns printed on wafer and in particular by line edge roughness
Implementation Method 2
an imager that comprises optics
Implementation Method 3
collecting radiation from the region
Data Source
AI summary
A method, system and computer readable medium for providing information about a region of a sample. The method includes (i) obtaining, by an imager, multiple images of the region; wherein the multiple images differ from each other by at least one parameter (ii) receiving or generating multiple reference images; (iii) generating multiple difference images that represent differences between the multiple images and the multiple reference images; (iv) calculating a set of region pixel attributes, (v) calculating a set of noise attributes, based on multiple sets of region pixels attributes of the multiple region pixels; and (vi) determining for each region pixel, whether the region pixel represents a defect based on a relationship between the set of noise attributes and the set of region pixel attributes of the pixel.