SEM Image Brightness Adjustment for Pattern-Specific Defect Detection
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Solution Overview
Problem
Existing semiconductor defect inspection methods using SEMs struggle to reliably detect defects in various patterns of semiconductor devices due to variations in pattern characteristics.
Innovation Solution
An image processing device and method that classifies SEM images by pattern type, adjusts brightness levels for each pattern, and compares gray levels to detect defects, utilizing a segmentation module, gray level adjustment module, and defect detection module to enhance defect detection reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single uniform brightness level is used for all patterns in semiconductor device inspection, then the inspection process is simple, but defect detection reliability deteriorates due to variations in pattern characteristics
Solution Approach 1:
The patent segments the semiconductor device image into multiple pattern groups based on pattern characteristics. Each pattern group is assigned a specific brightness level independently, allowing optimized defect detection for each pattern type while maintaining overall system manageability through structured segmentation.
Solution Approach 2:
The patent applies different brightness levels to different pattern groups according to their specific characteristics. This local quality approach ensures that each pattern type receives the optimal brightness treatment for its defect detection needs, rather than applying a uniform brightness level across all patterns.
2Measurement precision
If brightness values are adjusted for each pattern type, then defect detection ability improves, but processing time and complexity increase
Solution Approach 1:
The patent performs preliminary classification of patterns into groups based on their characteristics before the actual defect detection process. This preliminary action organizes the data structure in advance, enabling efficient brightness adjustment during defect detection without excessive processing time during the critical inspection phase.
3Measurement precision
If uniform image processing is applied to all patterns, then processing efficiency is maintained, but defect detection accuracy deteriorates due to ignoring pattern-specific characteristics
Solution Approach 1:
The patent changes the brightness level parameter specifically for each pattern group based on their characteristics. This parameter change enables accurate defect detection for different pattern types while the systematic approach to parameter adjustment maintains processing efficiency through automated and structured implementation.
Data Source
AI summary
An imaging device and an image processing method are provided. An imaging device according to some embodiments includes a memory and a processor which executes programs stored in the memory. The processor acquires a plurality of first images classified for each pattern of a semiconductor device, by using a SEM (Scanning Electron Microscope) image of the semiconductor device, and acquires a plurality of reference images in which brightness values are adjusted for each pattern of the semiconductor device by using the plurality of first images.