Deep learning's impact on pattern matching for Design Based Metrology and Design Based Inspection

摘要

With the miniaturization of devices, hot spot evaluation of a wide area of a wafer for small change points such as wafertopology is required. DBM (Design Based Metrology) is an effective method for evaluating systematic defects ofmultiple patterning and EUV lithography. However, it takes a long time to evaluate because it is necessary to acquire ahigh-SN SEM image captured by low-speed SEM scanning conditions. Therefore, we developed a new pattern matchingmethod of DBM by utilizing deep learning technology. Our proposed method can handle low-SN SEM images capturedunder high-speed SEM scanning conditions.In the proposed method, we use deep learning to estimate design layout from SEM image, and then perform patternmatching between this estimated design layout and the true design layout. The proposed method is particularly effectivefor pattern matching of low-SN SEM images and circuit pattern distorted during manufacturing process. It is expectedthat this method will be advantageous for evaluating mass systematic defects during the process development.Experimental results showed that the proposed method could estimate the design layout from the low-SN SEM imageand improve the pattern matching success rate.