Vol. 176
Latest Volume
All Volumes
PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
2023-01-08
A Novel Optical Proximity Correction (OPC) System Based on Deep Learning Method for the Extreme Ultraviolet (EUV) Lithography
By
Progress In Electromagnetics Research, Vol. 176, 95-108, 2023
Abstract
As one of the most important technologies for the next generation very-large scale integrated circuit fabrication, extreme ultraviolet (EUV) lithography has attracted more and more attention in recent years. However, in EUV lithography, the optical distortion of the printed image on wafer always has negative impacts on the imaging performance. Thus, to enhance the imaging performance of EUV system, especially for small critical dimensions, in this work, a novel optical proximity correction (OPC) system based on the deep learning technique is proposed. It includes a forward module and an inverse module, where the forward module is employed to fast and accurately map the mask to the corresponding near field of the plane above the stack to help the construction of training dataset for the inverse module operation, and the inverse module is employed to fast and accurately map the target printed image to the corrected mask. Numerical examples demonstrate that compared with traditional full-wave simulation, the forward module can greatly improve the computational efficiency including the required running time and memory. Meanwhile, different from time consuming iterative OPC methods, the corrected mask can be immediately obtained as the target printed image is input using the trained inverse module.
Citation
Li-Ye Xiao Jun-Nan Yi Yiqian Mao Xin-Yue Qi Ronghan Hong Qing Huo Liu , "A Novel Optical Proximity Correction (OPC) System Based on Deep Learning Method for the Extreme Ultraviolet (EUV) Lithography," Progress In Electromagnetics Research, Vol. 176, 95-108, 2023.
doi:10.2528/PIER22101601
http://www.jpier.org/PIER/pier.php?paper=22101601
References

1. Ma, X., Z. Wang, X. Chen, Y. Li, and G. R. Arce, "Gradient-based source mask optimization for extreme ultraviolet lithography," IEEE Trans. Comput. Imag., Vol. 5, No. 1, Mar. 2019.

2. Erdmann, A., P. Evanschitzky, F. Shao, T. Fühner, G. F. Lorusso, E. Hendrickx, M. Goethals, R. Jonckheere, T. Bret, and T. Hofmann, "Predictive modeling for EUV-lithography: The role of mask, optics, and photoresist effects," Proc. SPIE, Vol. 8171, No. 37, 23-33, Oct. 2011.

3. Cain, J., P. Naulleau, and C. Spanos, "Modeling of EUV photoresists witha resist point spread function," Proc. SPIE, Vol. 5751, 1101-1109, 2005.
doi:10.1117/12.600439

4. Ma, X., J. Wang, X. Chen, Y. Li, and G. R. Arce, "Gradient-based inverseextreme ultraviolet lithography," Appl. Opt., Vol. 54, No. 24, 7284-300, Aug. 2015.
doi:10.1364/AO.54.007284

5. Song, H., L. Zavyalova, I. Su, J. Shiely, and T. Schmoeller, "Shadowing effect modeling and compensation for EUV lithography," Proc. SPIE, Vol. 7969, No. 79691O, 2011.

6. Ma, X. and G. R. Arce, Computational Lithography, 1st Ed., Wiley Series in Pure and Applied Optics, John Wiley and Sons, New York, 2010.
doi:10.1002/9780470618943

7. Poonawala, A. and P. Milanfar, "Double-exposure mask synthesis using inverse lithography," Journal of Microlithography Microfabrication & Microsystems, Vol. 6, No. 4, 241-246, 2007.

8. Cobb, N. and D. Dudau, "Dense OPC and verification for 45 nm," Proc. SPIE, Vol. 6154, No. 61540I, Mar. 2006.

9. Sherif, S., B. Saleh, and R. Leone, "Binary image synthesis using mixed integer programming," EEE Trans. on Image Proc., Vol. 4, 1252-1257, 1995.
doi:10.1109/83.413169

10. Liu, Y. and A. Zakhor, "Binary and phase shifting mask design for optical lithography," IEEE Trans. Semicond. Manuf., Vol. 5, No. 2, 138-152, 1992.
doi:10.1109/66.136275

11. Granik, Y., "Solving inverse problems of optical microlithography," Proc. SPIE, Vol. 5754, 506-526, 2004.

12. Granik, Y., "Fast pixel-based mask optimization for inverse lithography," J. Microlith. Microfab. Microsyst., Vol. 5, No. 4, 043002, 2006.

13. Jia, N., A. K. Wong, and E. Y. Lam, "Robust mask design with defocus variation using inverse synthesis," Proc. SPIE, Lithography Asia, Vol. 7714, No. 71401W, 2008.

14. Shen, Y., N. Jia, N. Wong, and E. Y. Lam, "Robust level-set-based inverse lithography," Opt. Express, Vol. 19, No. 6, 5511-5521, 2011.
doi:10.1364/OE.19.005511

15. Shen, Y., N. Wong, and E. Y. Lam, "Aberration-aware robust mask design with level-set-based inverse lithography," Proc. SPIE, Vol. 7748, No. 77481U, 2010.

16. Wong, A. K., Resolution Enhancement Techniques in Optical Lithography, SPIE Press, 2001.
doi:10.1117/3.401208

17. Frye, R., E. Rietman, and K. Cummings, "Neural network proximity effect corrections for electron beam lithography," IEEE International Conference on Systems, Man and Cybernetics Conference Proceedings, 704-706, 1990.
doi:10.1109/ICSMC.1990.142210

18. Jedrasik, P., "Neural networks application for OPC (optical proximity correction) in mask making," Microelectron. Eng., Vol. 30, 1-4, 1996.

19. Huang, W. C., C. M. Lai, B. Luo, C. K. Tsai, M. H. Chih, C. W. Lai, C. C. Kuo, R. G. Liu, and H. T. Lin, "Intelligent model-based OPC," Proc. SPIE, Vol. 6154, 1065-1073, 2006.

20. Zeng, N., P. Wu, Z. Wang, H. Li, W. Liu, and X. Liu, "A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection," IEEE Trans. Instrum. Meas., Vol. 71, Article No. 3507014, 2022.

21. Mao, Y., Q. Zhan, R. Zhang, D. Wang, W.-F. Huang, and Q. H. Liu, "Fast simulation of electromagnetic fields in doubly periodic structures with a deep fully convolutional network," IEEE Trans. Antennas Propag., Vol. 69, No. 5, 2921-2928, 2021.
doi:10.1109/TAP.2020.3030940

22. Shim, S., S. Choi, and Y. Shin, "Machine learning (ML)-based lithography optimizations," 2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Jeju, South Korea, 2016.

23. Park, J. W., A. Torres, and X. Song, "Litho-Aware machine learning for hotspot detection," IEEE Trans. on Comp.-Aided Design of Integrated Circuits and Systems, Vol. 37, No. 7, 1510-1514, Jul. 2018.
doi:10.1109/TCAD.2017.2750068

24. Ma, X., S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, "A fast and manufacture-friendly optical proximity correction based on machine learning," Microelectron. Eng., Vol. 168, 15-26, 2017.
doi:10.1016/j.mee.2016.10.006

25. Mao, Y., J. Niu, Q. Zhan, R. Zhang, W.-F. Huang, and Q. H. Liu, "Calderόn preconditioned spectral-element spectral-integral method for doubly periodic structures in layered media," IEEE Trans. Antennas Propag., Vol. 68, No. 7, 5524-5533, Jul. 2020.
doi:10.1109/TAP.2020.2976584

26. Ronneberger, O., P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," International Conference on Medical Image Computing and Computer-assisted Intervention, 234-241, Springer, 2015.

27., , http://www.wavenology.com/?page id=66.