A methodology to weight OPC modeling data points

Chi-Yuan Hung; Ching-Heng Wang; Qingwei Liu; Cliff Ma; KeChih Wu; Gary Zhang

doi:10.1117/12.681819

20 May 2006 A methodology to weight OPC modeling data points

Chi-Yuan Hung, Ching-Heng Wang, Qingwei Liu, Cliff Ma, KeChih Wu, Gary Zhang

Proceedings Volume 6283, Photomask and Next-Generation Lithography Mask Technology XIII; 628331 (2006) https://doi.org/10.1117/12.681819
Event: Photomask and Next Generation Lithography Mask Technology XIII, 2006, Yokohama, Japan

Abstract

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 (as Fig.1), which makes the fitting process more time consuming and also may cause losing convergence and stability. This work is to weight different wafer data points with a weighting function. The weighting function is dependent on the deviation (or range or other statistical index) values for each measurable symmetric feature in the sampling space of the model fitting. Using this approach, we can filter wrong information of the process and make the OPC model more accurate (as Fig.2). NanoScope-Modeler is the platform we used in this study, which has been proven to have an excellent performance on 0.13μm, 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.

Citation Download Citation

Chi-Yuan Hung, Ching-Heng Wang, Qingwei Liu, Cliff Ma, KeChih Wu, and Gary Zhang "A methodology to weight OPC modeling data points", Proc. SPIE 6283, Photomask and Next-Generation Lithography Mask Technology XIII, 628331 (20 May 2006); https://doi.org/10.1117/12.681819

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