Defect Characterization Method for Semiconductor Yield
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Solution Overview
Problem
Existing semiconductor manufacturing processes fail to effectively detect and characterize specific defects, leading to reduced yield due to the miniaturization of defects, which are increasingly tiny and difficult to identify using current Automatic Defect Review (ADR) methods.
Innovation Solution
A defect characterization method that involves obtaining a first scanning image, marking a noise area to distinguish it from the defect area, performing ADR calculation, and obtaining a defect image with the maximum pixel level value to classify specific defects, thereby improving defect identification and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ADR methods are used to detect defects, then the detection process is simple, but the measurement precision deteriorates due to miniaturization of defects making them increasingly tiny and difficult to identify
Solution Approach 1:
The patent segments the defect detection process into multiple stages: initial scanning to locate defect areas, followed by detailed scanning of specific regions of interest. This segmentation allows the system to maintain high measurement precision by focusing detailed analysis only on areas where defects are suspected, rather than attempting to detect all defects uniformly across the entire wafer.
Solution Approach 2:
The patent introduces a hierarchical dimension to defect detection by implementing multiple scanning resolutions. The system performs a first scan at lower resolution to identify potential defect areas, then performs a second scan at higher resolution on those specific areas. This dimensional approach to resolution enables precise detection of tiny defects while managing the overall complexity of the detection process.
2Measurement precision
If detailed scanning is performed on the entire wafer to identify tiny defects, then the measurement precision improves, but the productivity deteriorates due to increased inspection time
Solution Approach 1:
The patent divides the wafer inspection into segments: a first scanning step that covers the entire wafer at lower resolution to identify defect areas, and a second scanning step that focuses only on identified defect areas at higher resolution. This segmentation enables the system to maintain high defect detection precision while significantly reducing the total inspection time compared to performing detailed scanning across the entire wafer surface.
Solution Approach 2:
The patent applies partial action by performing detailed high-resolution scanning only on portions of the wafer where defects are suspected, rather than performing exhaustive scanning on the entire wafer. This approach achieves sufficient defect detection precision for critical areas while maintaining productivity by avoiding unnecessary detailed inspection of defect-free regions.
3Measurement precision
If noise areas are not marked and separated, then the processing is simpler, but the measurement precision deteriorates due to interference from noise areas in defect characterization
Solution Approach 1:
The patent segments the image processing by marking and separating noise areas from defect areas. The system identifies and marks noise areas in the scanned images, then performs defect characterization only on unmarked areas. This segmentation improves measurement precision by eliminating noise interference while keeping the processing complexity manageable through automated noise identification algorithms.
Solution Approach 2:
The patent extracts and isolates noise areas from the defect analysis process by marking them separately. By taking out the noise areas and excluding them from defect characterization calculations, the system achieves higher precision in defect measurement without requiring extremely complex processing, as the noise removal is handled through systematic marking and exclusion rather than sophisticated filtering algorithms.
Data Source
AI summary
A defect characterization method includes: a first scanning image and target defect coordinates in the first scanning image are obtained; a first defect image is obtained according to the target defect coordinates in the first scanning image, the first defect image containing a defect area where a target defect is located and a noise area not containing the target defect; the noise area is marked, Automatic Defect Review (ADR) calculation is performed on the defect area, and a pixel level value of a defect in the defect area is obtained; coordinates of the defect with a maximum pixel level value are obtained, and a second defect image is obtained according to the coordinates of the defect with the maximum pixel level value; and the defect with the maximum pixel level value is classified according to the second defect image.