Automated SRAF printing detection and quantification through SEM image analysis

Abstract

The insertion of sub-resolution assist features as part of the optical proximity correction flow is one of the main elements of advanced resolution enhancement techniques. It needs to be ensured that there is no unintentional printing of such a sub-resolution assist feature in the photoresist since it would detract the product yield. To be able to prevent unintentional printings, a robust printing prediction simulation model is required. The calibration of such a model can be challenging since common CD-SEM measurements cannot be used to quantify such small sized printings at a poor contrast. The assessment of SEM images through human judgement is inefficient and somewhat subjective to errors. In this work, we propose an automated method to quantify SRAF printing through layout assisted SEM image analysis. The motivation is to get a fast and objective model calibration thanks to the automation and the absence of human judgement.