Self-Learning and Transfer across Topologies of Constraints for Analog / Mixed-Signal Circuit Layout Synthesis

Truly full automation of analog/mixed-signal (AMS) integrated circuit design and layout has long been a target in electronic design automation. Making good use of human designer heuristics as constraints that steer today's tools is key to balancing efficiency and design space exploration. However, explicitly getting the constraints for every circuit from designers is the weak spot. Learning-based methods on the other hand can learn efficiently from training examples. This paper proposes a flexible framework that can self-learn various layout constraints for a circuit from some expert-generated example layouts. Constraints like alignment, symmetry, and device matching are learned from those expert layouts with the generate-and-aggregate methodology. Secondly, through feature matching, the learned knowledge can then be transferred as constraints for the layout synthesis of different circuit topologies, making the approach flexible and technology-agnostic. Experimental results show that our framework can learn constraints with 100% accuracy. Compared to other state-of-the-art tools, our framework also achieves a high efficiency and a high transfer accuracy over various types of constraints.