The feasibility of using image parameters for test pattern selection during OPC model calibration

Abstract

Model based optical proximity correction (MB-OPC) is essential for the production of advanced integrated circuits (ICs). Calibration of these OPC resist models uses empirical fitting of measured test pattern data. It seems logical that to produce OPC models, acquiring more data will always improve the OPC model accuracy; on the other hand, reducing metrology and model build time is also a critical and continually escalating requirement with the constant increase in the complexity of the IC development process. A trade off must therefore be made to obtain adequate number of data points that produce accurate OPC models without overloading the metrology tools and resources. In this paper, we are examining the feasibility of using the image parameters (IPs) to select the test patterns. The approach is to base our test pattern selection only on the IPs and verify that the resulting OPC model is accurate. Another approach is to reduce the data gradually in different steps using IP considerations and see how the OPC model performance changes. A third, compromise approach is to specify a test pattern set based on IPs and add to that set few patterns based on different considerations. The three approaches and their results are presented in details in this paper.