Benchmarking study of 3D mask modeling for 2X and 1X nodes

ChangAn Wang; Chao-Chun Liang; Huikan Liu; Chidam Kallingal; Derren Dunn; James Oberschmidt; Jaione Tirapu Azpiroz

doi:10.1117/12.2011485

12 April 2013 Benchmarking study of 3D mask modeling for 2X and 1X nodes

ChangAn Wang, Chao-Chun Liang, Huikan Liu, Chidam Kallingal, Derren Dunn, James Oberschmidt, Jaione Tirapu Azpiroz

Proceedings Volume 8683, Optical Microlithography XXVI; 86831A (2013) https://doi.org/10.1117/12.2011485
Event: SPIE Advanced Lithography, 2013, San Jose, California, United States

Abstract

With ever shrinking critical dimensions, half nm OPC errors are a primary focus for process improvement in computational lithography. Among many error sources for 2x and 1x nodes, 3D mask modeling has caught the attention of engineers and scientists as a method to reduce errors at these nodes. While the benefits of 3D mask modeling are well known, there will be a runtime penalty of 30-40% that needs to be weighed against the benefit of optical model accuracy improvements. The economically beneficial node to adopt 3D mask modeling has to be determined by balancing these factors. In this paper, a benchmarking study has been conducted on a 20nm cut mask, metal and via layers with two different computational lithography approaches as compared with standard thin-mask approximation modeling. Besides basic RMS error metrics for model calibration and verification, through pitch and through size optical proximity behavior, through focus model predictability, best focus prediction and common DOF prediction are thoroughly evaluated. Runtime impact and OPC accuracy are also studied.

Citation Download Citation

ChangAn Wang, Chao-Chun Liang, Huikan Liu, Chidam Kallingal, Derren Dunn, James Oberschmidt, and Jaione Tirapu Azpiroz "Benchmarking study of 3D mask modeling for 2X and 1X nodes", Proc. SPIE 8683, Optical Microlithography XXVI, 86831A (12 April 2013); https://doi.org/10.1117/12.2011485

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KEYWORDS

Photomasks

3D modeling

Optical proximity correction

Performance modeling

Data modeling

Calibration

Critical dimension metrology

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