SRAF insertion via supervised dictionary learning

Abstract

In modern VLSI design flow, sub-resolution assist feature (SRAF) insertion is one of the resolution enhancement techniques (RETs) to improve chip manufacturing yield. With aggressive feature size continuously scaling down, layout feature learning becomes extremely critical. In this paper, for the first time, we enhance conventional manual feature construction, by proposing a supervised online dictionary learning algorithm for simultaneous feature extraction and dimensionality reduction. By taking advantage of label information, the proposed dictionary learning engine can discriminatively and accurately represent the input data. We further consider SRAF design rules in a global view, and design an integer linear programming model in the post-processing stage of SRAF insertion framework. Experimental results demonstrate that, compared with a state-of-the-art SRAF insertion tool, our framework not only boosts the mask optimization quality in terms of edge placement error (EPE) and process variation (PV) band area, but also achieves some speed-up.