Machine learning-based algorithm to accurately predict detail-route drvs for efficient design closure at advanced technology nodes

Abstract

A machine learning (ML) system is trained to predict the number of design rules violations of a circuit design that includes a multitude of Gcells. To achieve this, a netlist associated with the circuit design is placed by a place and route tool. A first list of features associated with the placed netlist is delivered to the ML system. A global route of the circuit design is performed by a global router. Next, a second list of features is delivered from the global router to the ML system. Thereafter, a detailed route of the circuit design is performed by a detailed router. A label associated with each Gcell in the circuit design is delivered to the ML system from the detailed route. The ML system is trained using the first and second list of features and the labels.