SVM-Based Critical Dimension Extraction for Semiconductor Nanostructures
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Solution Overview
Problem
Current optical scatterometer methods for extracting critical dimensions of semiconductor nanostructures are inefficient due to the need for extensive database searches, which become impractical as the number of simulation spectra grows, limiting scalability and accuracy.
Innovation Solution
A method utilizing Support Vector Machines (SVMs) to create a smaller electronic spectra database by dividing parameter value ranges into sub-ranges, generating simulation spectra, and employing a searching algorithm to find the optimum simulation spectrum, thereby reducing search time and improving extraction speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the simulation spectra database is expanded to improve measurement precision, then the accuracy of extracted parameters is improved, but the search time and computational complexity increase exponentially
Solution Approach 1:
The patent segments the large simulation spectra database into multiple sub-databases using clustering analysis. Each sub-database contains spectra with similar characteristics grouped together. This segmentation reduces the search space from the entire database to only relevant sub-databases, thereby maintaining measurement precision while significantly reducing search time and computational complexity.
Solution Approach 2:
The patent performs preliminary clustering analysis on the simulation spectra database to organize spectra into predefined sub-databases before the actual measurement process. This preliminary action creates an optimized data structure that enables faster searching during parameter extraction, as the system only needs to search within pre-grouped sub-databases rather than the entire database.
2Measurement precision
If traditional library-matching methods are used to ensure global optimal results, then measurement accuracy is maintained, but the method becomes impractical for large-scale databases
Solution Approach 1:
The patent divides the large simulation spectra database into multiple smaller sub-databases through clustering. This segmentation maintains scalability by allowing the system to handle large databases through modular sub-databases, while still achieving accurate results by searching within the relevant sub-database that contains the optimal match.
Solution Approach 2:
The patent applies local quality by focusing the search on specific sub-databases that are most relevant to the measured spectrum, rather than searching the entire database uniformly. This localized search approach maintains high accuracy by concentrating computational resources on the most promising regions of the parameter space while improving scalability.
3Productivity
If GPU hardware acceleration is used to improve data processing speed, then extraction speed increases, but hardware cost and system complexity increase
Solution Approach 1:
The patent replaces the need for GPU hardware acceleration with a software-based clustering algorithm approach. By organizing the database into sub-databases through clustering, the system achieves fast extraction speeds through intelligent data organization and reduced search space, eliminating the requirement for expensive GPU hardware while maintaining high productivity.
Data Source
AI summary
A method for extracting a critical dimension of a semiconductor nanostructure. The method includes: 1) determining a value range for each parameter to be extracted, whereby generating an electronic spectra database, and employing training spectra and support vector machine (SVM) training networks for training of SVMs; 2) employing the SVMs after training to map measured spectra to yield a corresponding electronic spectra database; and 3) employing a searching algorithm to search for an optimum simulation spectrum in the corresponding electronic spectra database, simulation parameters corresponding to the simulation spectrum being the critical dimension of the semiconductor nanostructure to be extracted.