1. 2A8 DRC with Deep Networks
Paul Franzon, Rhett Davis, Dror Baron
The design rule check (DRC) problem is getting too complex as the number of rules increases. The process takes a long time and needs an interactive DRC. In this project, we explore the feasibility of using machine learning to the DRC problem. In year 2, we will investigate link with yield monitors. All work will be done with NCSU15 nm PDK.
Problem Statement
Project Objectives
Demonstrate that DRC can be done with trained ML models
Based on extracted features
Experiment with different networks
Increase amount of data
After success with training
Expand rule set to full 15 nm PDK
Investigate connection to yield monitor data
In year 2:
Investigate deriving rules from yield monitor data, OR
Investigate driving semi-automated layout
Took ~50 SRAM desgins from class project
Laid out in 15 nm PDK
Gds files of layouts are converted to gdt. 200 variants of each gdt file are generated by randomly changing the widths of M1 metal boundaries.
These gdt files are then used to generate images.
Each layout is also passed through Calibre DRC tool, to obtain a result-file that lists the locations of DRC violations, labled by an error mask
Trained two layer network with 20,000 images
Very quick to train
Achieved 80% success rate on retained data
Proof of Concept
Technical Approach
Year 1:
Work on extracted features, not raw pixelated image data
E.g. layout shape coordinates
Greatly reduces data set size
Likely to increase success rate
Increase training set
Investigate different networks and parameters
Extend to other rules
Year 2:
Investigate connection to yield monitors
Train same networks from yield monitors, instead of labelled DRC rules OR
Investigate driving layout
Treat layout as a parameterized model
Original Layout
Variation #1
Variation #2
Layout
Error Mask
Future Scope
New paradigm for DFM management