1. DRC with Deep Networks Tanmay Lagare, Arpit Jain, Luis Francisco,
Madhura Kulkarni, Somal Chaudhary, Divya Sardana
Facullty: Rhett Davis, Paul Franzon
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. Currently, we could train a convolutional neural network (CNN) to detect only one DRC Rule, (M1.1- Minimum width of M1 metal layer is 28nm) with an accuracy of 75-80%.
Abstract
CNN Details
A Convolutional Neural Network is trained with images picked randomly from the generated dataset.
It is a basic network with only two layers.
Learning rate is set at
The hidden layer uses the ReLU activation function and output layer uses softmax function.
The network is trained for epochs.
It was observed that the accuracy improves with an increase in the number of images used for training. This is because the number of features in an image are less.
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.
Dataset Generation
Generated a set of 5000 layouts from given set of 25 layouts.
Performed windowing of the layouts to get a dataset of 5 million labelled images.
A training accuracy of around 80% is achieved with 20,000 input images.
Validate that it is possible to detect DRC errors in a layout using machine learning.
Results
Original Layout
Variation #1
Variation #2
Windowing and Labeling
5000 images generated from the layouts are of the order of 5000 x 5000 pixels, which is too large for the deep network to process.
These large images are divided into smaller images by moving a window of size 200x200 pixels with a stride of 150 pixels, to create a dataset of 5 million images
Result files from Calibre-run are used to draw a mask image, that identifies the locations of DRC violations in a layout.
This mask image is used to label the individual windows.
Future Scope
A different network can be used to train a model and compare the results with the current CNN model.
The number of layers and/or nodes can be increased to obtain better results.
Train the network for different types of DRC rules and check the potentials and limitations of applying the machine learning model in identifying DRC violations.
Perform feature extraction first so that the network does not have to deal with a large amount of data.
Layout
Mask