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About 草榴社区 S-Metro
草榴社区 S-Metro provides a comprehensive and powerful toolbox for automated assessment of numerical and image-based metrology data for photomask qualification, modeling data preparation, and process characterization. Process engineers use it to measure the lithographic performance, for instance through process window analysis.?
Metrology images from any source, whether photomask or wafer, can be processed and analyzed in 草榴社区 S-Metro as well. Images are aligned to the corresponding layout and can be averaged to improve contrast; contours are extracted for use in model calibration and validation as well as for training machine learning modes.?
草榴社区 S-Metro automatically processes SEM image and CD data in a consistent and transparent manner. The infrastructure is capable of handling tens of thousands of data points and scales well on compute clusters.
Key Benefits
Robust Contour Extraction
Comprehensive Analysis
Full Automation
Big Data Handling
Features
Quickly Import and Assess Gauge-based Measurement Results to Characterize Lithographic Performance
- Automate data import from Hitachi (*.msr) and AMAT (*.cst) CD-SEM
- Sort, group, and filter data for quick navigation
- Utilize associated SEM images for data validation and flyer elimination
- Determine individual and overlapping process windows, including rectangular or elliptical fits
- Assess data consistency through statistical analysis
Quickly Import and Analyze SEM Images to Qualify Photomask or Generate Data for Building Lithographic Models
- Automated import of SEM images from any source
- Fast and accurate alignment of images to layout (GDS, OASIS)
- Robust and customizable contour extraction
- Extensive contour analysis capabilities
- Intra-field averaging
- CD, 2D area, pitch, EPE assessment
- Contour to CD matching
- S-Litho compatible gauge-based analysis for line/space pattern and contact holes
- Export of extracted contours to GDS or OASIS format for subsequent litho process simulation
- Automation and parallelization
- Scripting via python, tcl; marco recording
- Highly efficient DP scaling on clusters for high volume data processing
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