Machine learning based generic violation waiver system with application on electromigration sign-off

Manually analyzing the results generated by EDA tools to waive or fix any violations is a tedious, error-prone and time-consuming process. By automating these time-consuming rigorous manual procedures by aggregating key insights across different designs using continuing and prior simulation data, a design team can speed up the tape-out process, optimize resources and significantly minimize the risk of overlooking must fix violations that are prone to cause field failures. In this paper, a machine learning based generic waiver system is proposed which continuously learns to improve with new design data using K-means clustering and nearest neighbor algorithms for risk scoring. The system has been used on new designs to demonstrate on-chip Electromigration (EM) waiver (EMWaiver) mechanism that yielded highly confident results.