Feature Extraction for Pattern Recognition in IC Manufacturing
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Solution Overview
Problem
Current techniques for pattern classification and selection in IC manufacturing are computationally intensive and inefficient, particularly in computational lithography, due to the need for extensive pattern data sets and complex models, which can be impractical for real-time defect prevention and optimization in IC design.
Innovation Solution
A method involving feature extraction by dividing pattern instances into zones, determining representative characteristics, and generating feature vectors to classify or select patterns, using data formats like GDS, OASIS, or CIF, and converting features into representative points, allowing for efficient pattern representation and analysis without requiring intensive computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive pattern data sets and complex models are used for pattern classification and selection, then pattern recognition accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The pattern instance is divided into multiple zones, and each zone is processed independently to extract local features. This segmentation approach reduces the overall computational complexity by breaking down the complex pattern recognition task into smaller, manageable sub-tasks that can be processed in parallel or sequentially with reduced resource requirements.
Solution Approach 2:
The invention extracts only the most relevant local features from pattern instances using hand-crafted feature extraction methods. By selecting and extracting specific representative characteristics from each zone rather than processing entire pattern data sets, the system reduces computational complexity while maintaining pattern recognition accuracy.
2Measurement precision
If extensive pattern data sets are used for classification, then classification accuracy is improved, but processing time and productivity decrease
Solution Approach 1:
The system extracts only essential local features from pattern instances using efficient hand-crafted feature extraction algorithms. This selective extraction of representative characteristics from each zone significantly reduces the amount of data that needs to be processed, thereby improving processing speed and productivity while maintaining classification accuracy.
Solution Approach 2:
By dividing pattern instances into zones and processing each zone independently, the system enables parallel processing of multiple regions simultaneously. This segmentation strategy reduces overall processing time and improves productivity without sacrificing the accuracy of pattern classification.
3Adaptability or versatility
If complex models are used for pattern analysis, then pattern coverage is improved, but computational resources and time requirements increase
Solution Approach 1:
The invention applies different feature extraction strategies to different zones of pattern instances based on their local characteristics. By tailoring the feature extraction approach to each specific zone's properties, the system achieves comprehensive pattern coverage and adaptability while avoiding the computational overhead of applying complex uniform models across entire patterns.
Data Source
AI summary
An apparatus and method of feature extraction for identifying a pattern. An improved method includes obtaining data representative of a pattern instance, dividing the pattern instance into a plurality of zones, determining a representative characteristic of a zone of the plurality of zones, generating a representation of the pattern instance using a feature vector, wherein the feature vector includes an element corresponding to the representative characteristic, wherein the representative characteristic is indicative of a spatial distribution of one or more features of the zone. The method may also include classifying and/or selecting pattern instances based on the feature vector.