Layout Symmetry Annotation for Analog Circuits with Graph Neural Networks

The performance of analog circuits is susceptible to various layout constraints, such as symmetry, matching, etc. Modern analog placement and routing algorithms usually need to take these constraints as input for high quality solutions, while manually annotating such constraints is tedious and requires design expertise. Thus, automatic constraint annotation from circuit netlists is a critical step to analog layout automation. In this work, we propose a graph learning based framework to learn the general rules for annotation of the symmetry constraints with path-based feature extraction and label filtering techniques. Experimental results on the open-source analog circuit designs demonstrate that our framework is able to achieve significantly higher accuracy compared with the most recent works on symmetry constraint detection leveraging graph similarity and signal flow analysis techniques. The framework is general and can be extended to other pairwise constraints as well.