Sub-resolution assist feature (SRAF) printing prediction using logistic regression

Abstract

In optical proximity correction (OPC), the sub-resolution assist feature (SRAF) has been used to enhance the process window of main structures. However, the printing of SRAF on wafer is undesirable as this may adversely degrade the overall process yield if it is transferred into the final pattern. A reasonably accurate prediction model is needed during OPC to ensure that the SRAF placement and size have no risk of SRAF printing. Current common practice in OPC is either using the main OPC model or model threshold adjustment (MTA) solution to predict the SRAF printing. This paper studies the feasibility of SRAF printing prediction using logistic regression (LR). Logistic regression is a probabilistic classification model that gives discrete binary outputs after receiving sufficient input variables from SRAF printing conditions. In the application of SRAF printing prediction, the binary outputs can be treated as 1 for SRAFPrinting and 0 for No-SRAF-Printing. The experimental work was performed using a 20nm line/space process layer. The results demonstrate that the accuracy of SRAF printing prediction using LR approach outperforms MTA solution. Overall error rate of as low as calibration 2% and verification 5% was achieved by LR approach compared to calibration 6% and verification 15% for MTA solution. In addition, the performance of LR approach was found to be relatively independent and consistent across different resist image planes compared to MTA solution.