Analysis of Security of Split Manufacturing using Machine Learning

Abstract

This work is the first to analyze the security of split manufacturing using machine learning, based on data collected from layouts provided by industry, with 8 routing metal layers, and significant variation in wire size and routing congestion across the layers. We consider many types of layout features for machine learning including those obtained from placement, routing, and cell sizes. For the top split layer, we demonstrate dramatically better results in proximity attack compared to a recent prior work. We analyze the ranking of the features used by machine learning and show the importance of how features vary when moving to the lower layers. Since the runtime of our basic machine learning becomes prohibitively large for lower layers, we propose novel techniques to make it scalable with little sacrifice in effectiveness of the attack.