A CNN-Based Technique to Assist Layout-to-Generator Conversion for Analog Circuits

TL;DR

This paper introduces a CNN-based method to automate and accelerate the process of converting reference layouts into procedural layouts for analog circuits by identifying sub-cells suitable for generation scripts.

Contribution

It presents a novel CNN model that automatically detects and classifies sub-cells for layout generation, significantly reducing examination time and handling unfamiliar sub-cells effectively.

Findings

Achieved 99.3% precision in classifying sub-cells.

Reduced examination time from 88 minutes to 18 seconds.

Successfully classified unfamiliar sub-cells outside training data.

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.