Evaluation of OPC quality using automated edge placement error measurement with CD-SEM

Optical proximity correction (OPC) plays a vital role in the lithography process of cutting-edge IC fabrication. The quality of lithography models used in OPC is fundamental to the final performance of the OPC in production. Traditionally, two-dimensional proximity features such as line-end, bar-to-bar or bar-to-line were only partially characterized because of the difficulty in transferring the SEM information into the OPC model building process. A new methodology of edge placement error (EPE) measurement using CD-SEM is proposed as part of an OPC model building and process/OPC qualification flow. It is not easy to generate EPE measurements because of the inherent need to overlay the design and the SEM in order to quantify EPE. The quality of the EPE measurement depends on both the accuracy of the SEM image scan rotation and magnification, but also on the accuracy of pattern matching between the design layout pattern and the realized pattern (wafer). These problems do not exist in simulation, but model calibration requires a direct comparison between simulation and measurement. Measuring EPE effectively brings the measurement information into the realm of the design. Hitachi High-Technologies has developed a "fully automated EPE measurement function" based on design layout and detected edges of SEM image as a solution to this issue. This study shows several practical evaluation results using the automated EPE measurement function. The applications that will be discussed are as follows. 1) Design based classification of edges and subsequent quantification of SEM EPE for many types of edge arrangement and orientation. In this study, we will examine line-end-adjacent, line-end, corner, and other critical gate edges. 2) SEM image based classification of EPE fliers as a new population of errors. 3) Comparison between the detected edge of the feature within the SEM image and a polygon shape generated by lithography simulation to determine the quality of the simulation. 4) Conversion of the SEM image edge contour into an OASIS file and construction of a process variability band to quantify CD variability for all 2D contexts in a SEM image.