Effective data sampling techniques for machine learning OPC in full chip production

Data sampling is a corner stone in any machine learning applications, and ML-OPC is no different. As feature resolution and process variations continue to shrink for new nodes of both DUV and EUV lithography, the amount of data that can be collected can be enormous, and smart advanced data sampling will be indeed needed. In last year's SPIE paper in Refs. 1, we explored how can we utilize parametric test patterns to augment random sampling to collect an improved training data set. Given that random sampling has been good enough for some older technology nodes, a new advanced way to sample the data became inevitable for newer nodes. In this paper, we explore a few techniques to improve the quality of the ML-OPC training data. We show how different sampling methodologies can affect the training and inference results. We start by exploring different techniques then compare the results of different sampling techniques using both geometrical and image parameters and compare to regular random sampling results. We will also show how the Proteus capsules made this work easy and accessible for the user. At the end, we will show how this work can be integrated in one Proteus Workflow (PWF) for easier exploration for the results.