Paper
22 March 2011 Optimizing OPC data sampling based on "orthogonal vector space"
Yuyang Sun, Yee Mei Foong, Yingfang Wang, Jacky Cheng, Dongqing Zhang, Shaowen Gao, Nanshu Chen, Byoung Il Choi, Antoine J. Bruguier, Mu Feng, Jianhong Qiu, Stefan Hunsche, Liang Liu, Wenjin Shao
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Abstract
With shrinking feature sizes and error budgets in OPC models, effective pattern coverage and accurate measurement become more and more challenging. The goal of pattern selection is to maximize the efficiency of gauges used in model calibration. By optimizing sample plan for model calibration, we can reduce the metrology requirement and modeling turn-around time, without sacrificing the model accuracy and stability. With the Tachyon pattern-selection-tool, we seek to parameterize the patterns, by assessing dominant characteristics of the surroundings of the point of interest. This allows us to represent each pattern with one vector in a finite-dimensional space, and the entire patterns pool with a set of vectors. A reduced but representative set of patterns can then be automatically selected from the original full set sample data, based on certain coverage criteria. In this paper, we prove that the model built with 56% reduced wafer data could achieve comparable quality as the model built with full set data.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuyang Sun, Yee Mei Foong, Yingfang Wang, Jacky Cheng, Dongqing Zhang, Shaowen Gao, Nanshu Chen, Byoung Il Choi, Antoine J. Bruguier, Mu Feng, Jianhong Qiu, Stefan Hunsche, Liang Liu, and Wenjin Shao "Optimizing OPC data sampling based on "orthogonal vector space"", Proc. SPIE 7973, Optical Microlithography XXIV, 79732K (22 March 2011); https://doi.org/10.1117/12.881575
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Cited by 11 scholarly publications and 1 patent.
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KEYWORDS
Data modeling

Vector spaces

Calibration

Optical proximity correction

Semiconducting wafers

Statistical modeling

Metrology

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