APPLE: An Explainer of ML Predictions on Circuit Layout at the Circuit-Element Level

In recent years, we have witnessed many excellent machine learning (ML) solutions targeting circuit layouts. These ML models provide fast predictions on various design objectives. However, almost all existing ML solutions have neglected the basic interpretability requirement from potential users. As a result, it is very difficult for users to figure out any potential accuracy degradation or abnormal behaviors of given ML models. In this work, we propose a new technique named APPLE to explain each ML prediction at the resolution level of circuit elements. To the best of our knowledge, this is the first effort to explain ML predictions on circuit layouts. It provides a significantly more reasonable, useful, and efficient explanation for lithography hotspot prediction, compared with the highest-cited prior solution for natural images.