🤖 AI Summary
Converting analog circuit reference layouts into programmable layout generators remains inefficient and labor-intensive. Method: This paper proposes a convolutional neural network (CNN)-based automated translation method that performs end-to-end learning to accurately classify layout subcell instances and automatically match them to an existing generator library, recommending their optimal hierarchical placement. Contribution/Results: To the best of our knowledge, this is the first work to apply CNNs to the “layout-to-generator” mapping task, significantly enhancing generalization—enabling accurate identification of unseen subcells with substantial structural divergence from training samples. Evaluated on a dataset of 4,885 instances, the method achieves 99.3% classification accuracy. Processing time per instance drops from 88 minutes manually to 18 seconds automatically, markedly improving layout reuse and generator-based layout synthesis efficiency for analog circuits.
📝 Abstract
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.