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16 March 2009 Smart data filtering for enhancement of model accuracy
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As integrated circuit technology advances and features shrink, the scale of critical dimension (CD) variations induced by lithography effects become comparable with the critical dimension of the design itself. At the same time, each technology node requires tighter margins for errors introduced in the lithography process. Optical and process models -- the black boxes that simulate the pattern transfer onto silicon -- are becoming more and more concerned with those different process errors. As a consequence, an optical proximity correction (OPC) model consists mainly of two parts; a physical part dealing with the physics of light and its behavior through the lithographical patterning process, and an empirical part to account for any process errors that might be introduced between writing the mask and sampling measurements of patterns on wafer. Understanding how such errors can affect a model's stability and predictability, and taking such errors into consideration while building a model, could actually help convergence, stability, and predictability of the model when it comes to design patterns other than those used during model calibration and verification. This paper explores one method to quickly enhance model accuracy and stability.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shady Abdelwahed, Jae Hyun Kang, Jaeyoung Choi, Jong Doo Kim, Hyesung Lee, Sungho Jun, and Youngmi Kim "Smart data filtering for enhancement of model accuracy", Proc. SPIE 7274, Optical Microlithography XXII, 727423 (16 March 2009);

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