Semiconductor Defect Classification via Critical Dimension Variation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing semiconductor wafer inspection systems face inefficiencies in detecting and classifying defects due to the diverse shapes and structures of patterns on semiconductor wafers, requiring extensive operator time and potentially missing significant defects during the fabrication process.
Innovation Solution
A method utilizing a distance transform function to determine critical dimension variations by comparing reference images to inspection images, identifying contours of interest, and measuring distances to classify defects based on critical dimension parameter values, thereby automating the defect classification process across various pattern types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection methods are used to detect and classify defects on semiconductor wafers, then operators can identify defects, but the process requires extensive operator time and may miss significant defects
Solution Approach 1:
The inspection system performs automatic defect detection and classification without requiring operator intervention for each defect. The system measures pattern dimensions, compares them to specifications, and classifies defects autonomously, eliminating the need for manual inspection while maintaining high reliability
Solution Approach 2:
The patent replaces manual mechanical inspection with an automated optical measurement system that uses image processing and automated dimension measurement to detect and classify defects, significantly reducing operator time while improving consistency and reliability
2Productivity
If automated measurement systems are implemented to reduce operator time, then inspection speed increases, but the system must accurately handle diverse pattern shapes and structures
Solution Approach 1:
The automated measurement system is designed to measure various pattern types including lines, spaces, holes, and complex structures using a unified measurement approach. The system automatically adapts to different pattern geometries by identifying relevant dimensions and measurement points, enabling high-speed inspection across diverse semiconductor patterns without requiring operator intervention
3Reliability
If comprehensive defect classification is performed to improve quality control, then more defects are identified and addressed, but the complexity of the inspection system increases
Solution Approach 1:
The defect classification process is segmented into distinct automated steps: dimension measurement, specification comparison, and defect classification. Each step handles a specific aspect of quality control, allowing comprehensive defect identification while maintaining system manageability through modular processing
Data Source
AI summary
Input data may be received. The input data may include an image of a pattern and location data that identifies a modified portion of the pattern. A processing device may determine a first parameter of a first dimension within the pattern and a second parameter of a second dimension outside of the pattern. A combined set may be generated based on the first parameter and the second parameter. A defect associated with the modified portion may be classified based on the combined set.