A CNN-Based Technique to Assist Layout-to-Generator Conversion for Analog Circuits
This work addresses efficiency in analog circuit design automation, though it is incremental as it builds on existing CNN methods for a specific domain task.
The paper tackles the problem of converting analog circuit layouts into procedural generators by using a CNN to automatically detect reusable sub-cells from a library, achieving 99.3% precision and reducing examination time from 88 minutes to 18 seconds.
We propose a technique to assist in converting a reference layout of an analog circuit into the procedural layout generator by efficiently reusing available generators for sub-cell creation. The proposed convolutional neural network (CNN) model automatically detects sub-cells that can be generated by available generator scripts in the library, and suggests using them in the hierarchically correct places of the generator software. In experiments, the CNN model examined sub-cells of a high-speed wireline receiver that has a total of 4,885 sub-cell instances including different 145 sub-cell designs. The CNN model classified the sub-cell instances into 51 generatable and one not-generatable classes. One not-generatable class indicates that no available generator can generate the classified sub-cell. The CNN model achieved 99.3% precision in examining the 145 different sub-cell designs. The CNN model greatly reduced the examination time to 18 seconds from 88 minutes required in manual examination. Also, the proposed CNN model could correctly classify unfamiliar sub-cells that are very different from the training dataset.