GeniusRoute: A New Analog Routing Paradigm Using Generative Neural Network Guidance

Abstract

Due to sensitive layout-dependent effects and varied performance metrics, analog routing automation for performance-driven layout synthesis is difficult to generalize. Existing research has proposed a number of heuristic layout constraints targeting specific performance metrics. However, previous frameworks fail to automatically combine routing with human intelligence. This paper proposes a novel, fully automated, analog routing paradigm that leverages machine learning to provide routing guidance, mimicking the sophisticated manual layout approaches. Experiments show that the proposed methodology obtains significant improvements over existing techniques and achieves competitive performance to manual layouts while being capable of generalizing to circuits of different functionality.