Optical Proximity Correction with Principal Component Regression

An important step in today’s Integrated Circuit (IC) manufacturing is optical proximity correction (OPC). In model based OPC, masks are systematically modified to compensate for the non-ideal optical and process effects of optical lithography system. The polygons in the layout are fragmented, and simulations are performed to determine the image intensity pattern on the wafer. Then the mask is perturbed by moving the fragments to match the desired wafer pattern. This iterative process continues until the pattern on the wafer matches the desired one. Although OPC increases the fidelity of pattern transfer to the wafer, it is quite CPU intensive; OPC for modern IC designs can take days to complete on computer clusters with thousands of CPU. In this paper, techniques from statistical machine learning are used to predict the fragment movements. The goal is to reduce the number of iterations required in model based OPC by using a fast and efficient solution as the initial guess to model based OPC. To determine the best model, we train and evaluate several principal component regression models based on prediction error. Experimental results show that fragment movement predictions via regression model significantly decrease the number of iterations required in model based OPC.