All OPC model builders are in search of a physically realistic model that is adequately calibrated and contains the information that can be used for process predictions and analysis of a given process. But there still are some unknown physics in the process and wafer data sets are not perfect. Most cases even using the average values of different empirical data sets will still take inaccurate measurements into the model fitting process, which makes the fitting process more time consuming and also may cause losing convergence and stability.
The Image quality is one of the most worrisome obstacles faced by next-generation lithography. Nowadays, considerable effort is devoted to enhance the contrast, as well as understanding its impact on devices. It is a persistent problem for 193nm micro-lithography and will carry us for at least three generations, culminating with immersion lithography.
This work is to weight different wafer data points with a weighting function. The weighting function is dependent on the Normal image log slope (NILS), which can reflect the image quality. Using this approach, we can filter wrong information of the process and make the OPC model more accurate.
CalibreWorkbench is the platform we used in this study, which has been proven to have an excellent performance on 0.13um, 90nm and 65nm production and development models setup. Leveraging its automatic optical-tuning function, we practiced the best weighting approach to achieve the most efficient and convergent tuning flow.