Efficient measurement and optical proximity correction modeling to catch lithography pattern shift issues of arbitrarily distributed hole layer

Yaobin FENG , Jiamin LIU , Zhiyang SONG , Hao JIANG , Shiyuan LIU

Front. Mech. Eng. ›› 2024, Vol. 19 ›› Issue (4) : 24

PDF (2955KB)
Front. Mech. Eng. ›› 2024, Vol. 19 ›› Issue (4) : 24 DOI: 10.1007/s11465-024-0795-1
RESEARCH ARTICLE

Efficient measurement and optical proximity correction modeling to catch lithography pattern shift issues of arbitrarily distributed hole layer

Author information +
History +
PDF (2955KB)

Abstract

With the continued shrinking of the critical dimensions (CDs) of wafer patterning, the requirements for modeling precision in optical proximity correction (OPC) increase accordingly. This requirement extends beyond CD controlling accuracy to include pattern alignment accuracy because misalignment can lead to considerable overlay and metal-via coverage issues at advanced nodes, affecting process window and yield. This paper proposes an efficient OPC modeling approach that prioritizes pattern-shift-related elements to tackle the issue accurately. Our method integrates careful measurement selection, the implementation of pattern-shift-aware structures in design, and the manipulation of the cost function during model tuning to establish a robust model. Confirmatory experiments are performed on a via layer fabricated using a negative tone development. Results demonstrate that pattern shifts can be constrained within a range of ±1 nm, remarkably better than the original range of ±3 nm. Furthermore, simulations reveal notable differences between post OPC and original masks when considering pattern shifts at locations sensitive to this phenomenon. Experimental validation confirms the accuracy of the proposed modeling approach, and a firm consistency is observed between the simulation results and experimental data obtained from actual design structures.

Graphical abstract

Keywords

computational lithography / optical proximity correction / modeling / pattern shift / metrology

Cite this article

Download citation ▾
Yaobin FENG, Jiamin LIU, Zhiyang SONG, Hao JIANG, Shiyuan LIU. Efficient measurement and optical proximity correction modeling to catch lithography pattern shift issues of arbitrarily distributed hole layer. Front. Mech. Eng., 2024, 19(4): 24 DOI:10.1007/s11465-024-0795-1

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

WongA K. Resolution Enhancement Techniques in Optical Lithography. Washington: SPIE, 2001, 153–168

[2]

MaX, ArceG R. Computational Lithography. New York: John Wiley & Sons, 2010, 10–17

[3]

Randall J, Gangala H, Tritchkov A. Lithography simulation with aerial image––variable threshold resist model. Microelectronic Engineering, 1999, 46(1–4): 59–63

[4]

Peng D, Hu P, Tolani V, Dam T, Tyminski J, Slonaker S. Toward a consistent and accurate approach to modeling projection optics. In: Optical Microlithography XXII. San Jose: SPIE, 2010, 7640: 76402Y

[5]

LiaoH, Palmer S, SadraK. Variable threshold optical proximity correction (OPC) models for high-performance 0.18-μm process. In: Optical Microlithography XIII. Santa Clara: SPIE Microlithography, 2000, 1033–1040

[6]

Roessler T, Frankowsky B, Toublan O. Improvement of empirical OPC model robustness using full-chip aerial image analysis. In: Proceedings of the 23rd Annual BACUS Symposium on Photomask Technology. Monterey: SPIE Photomask Technology, 2003, 5256: 222–229

[7]

Poonawala A, Milanfar P. Mask design for optical microlithography-an inverse imaging problem. IEEE Transactions on Image Processing, 2007, 16(3): 774–788

[8]

Ma X, Arce G R. Pixel-based OPC optimization based on conjugate gradients. Optics Express, 2011, 19(3): 2165–2180

[9]

Granik Y, Medvedev D, Cobb N. Towards standard process models for OPC. In: Proceedings of Optical Microlithography XX. San Jose: SPIE Advanced Lithography, 2007, 6520: 652043

[10]

Granik Y, Cobb N, Medvedev D. Extreme mask corrections: technology and benefits. In: Optical Microlithography XXI. San Jose: SPIE Advanced Lithography, 2008, 6924: 69243W

[11]

Bahnas M, Al-Imam M. OPC model calibration considerations for data variance. In: Optical Microlithography XXI. San Jose: SPIE Advanced Lithography, 2008, 6924: 69243U

[12]

Kusnadi I, Do T, Granik Y, Sturtevant J L, De Bisschop P, Hibino D. Contour based self-aligning calibration of OPC models. In: Metrology, Inspection, and Process Control for Microlithography XXIV. San Jose: SPIE Advanced Lithography, 2010, 7638: 76382M

[13]

Jayaram S, LaCour P, Word J, Tritchkov A. Model-based SRAF solutions for advanced technology nodes. In: Proceedings of SPIE 29th European Mask and Lithography Conference, 2013, 8886: 88860P

[14]

Zuniga C, Deng Y. Resist toploss modelling for OPC applications. In: Optical Microlithography XXVII. San Jose: SPIE Advanced Lithography, 2014, 9052: 905227

[15]

TaravadeK N, Croffie E H, JostA. Two-dimensional image based model calibration for OPC applications. In: Optical Microlithography XVII. Santa Clara: SPIE Microlithography, 2004, 1522–1527

[16]

Tabery C, Morokuma H, Matsuoka R, Page L, Bailey G E, Kusnadi I, Do T. SEM image contouring for OPC model calibration and verification. In: Optical Microlithography XX. San Jose: SPIE Advanced Lithography, 2007, 6520: 652019

[17]

Granik Y, Kusnadi I. Challenges of OPC model calibration from SEM contours. In: Metrology, Inspection, and Process Control for Microlithography XXII. San Jose: SPIE Advanced Lithography, 2008, 6922: 69221H

[18]

Filitchkin P, Do T, Kusnadi I, Sturtevant J L, De Bisschop P, Van de Kerkhove J. Contour quality assessment for OPC model calibration. In: Metrology, Inspection, and Process Control for Microlithography XIII. San Jose: SPIE Advanced Lithography, 2009, 7272: 72722Q

[19]

Hibino D, Shindo H, Abe Y, Hojyo Y, Fenger G, Do T, Kusnadi I, Sturtevant J L, De Bisschop P, Van de Kerkhove J. High accuracy OPC-modeling by using advanced CD-SEM based contours in the next generation lithography. In: Metrology, Inspection, and Process Control for Microlithography XXIV. San Jose: SPIE Advanced Lithography, 2010, 7638: 76381X

[20]

Kim Y, Lee S, Hou Z, Zhao Y, Liu M, Zheng Y, Zhao Q, Kang D, Wang L, Simmons M, Feng M, Lang J, Choi B, Kim G, Sim H, Park J, Yoo G, Lee J, Ko S, Choi J, Kim C, Park C. OPC model accuracy study using high volume contour based gauges and deep learning on memory device. In: Metrology, Inspection, and Process Control for Microlithography XXXIII. San Jose: SPIE Advanced Lithography, 2019, 10959: 1095913

[21]

Weisbuch F, Jantzen K. Enabling scanning electron microscope contour-based optical proximity correction models. Journal of Micro/Nanolithography, MEMS, and MOEMS, 2015, 14(2): 021105

[22]

Fischer D, Han G, Oberschmidt J, Cheng Y W, Maeng J Y, Archie C, Lu W, Tabery C. Challenges of implementing contour modeling in 32 nm technology. In: Metrology, Inspection, and Process Control for Microlithography XXII. San Jose: SPIE Advanced Lithography, 2008, 6922: 69220A

[23]

Chuyeshov C, Carrero J, Sezginer A, Kamat V. Calibration of e-beam and etch models using SEM images. In: Photomask Technology. Monterey: SPIE, 2009, 7488: 74883I

[24]

De Bisschop P, Van de Kerkhove J. Alignment and averaging of scanning electron microscope image contours for optical proximity correction modeling purposes. Journal of Micro/Nanolithography, MEMS, and MOEMS, 2010, 9(4): 041302

[25]

Hopkins H H. The concept of partial coherence in optics. Proceedings of the Royal Society of London Series A: Mathematical and Physical Sciences, 1951, 208(1093): 263–277

[26]

Drozdov A N, Kempsell M L, Granik Y. Fitness and runtime correlation of compact model complexity. In: Optical Microlithography XXI. San Jose: SPIE Advanced Lithography, 2008, 6924: 692445

[27]

Kumar P, Srinivasan B, Mohapatra N R. Fast and accurate lithography simulation using cluster analysis in resist model building. Journal of Micro/Nanolithography, MEMS, and MOEMS, 2015, 14(2): 023506

[28]

Yuan W, Lu Y, Zhao Y, Chen S, Li M, Hu H, Yao S, Liu Z, Li Q, Tian Y, Zhou Z, Gu L, Wang J, Sheng X, Yan G, Zheng Y, Yao Y, Xiao Y, Liu L, Zhao Q, Feng M, Chen J, Lang J. Metrology and deep learning integrated solution to drive OPC model accuracy improvement. In: Optical Microlithography XXXII. San Jose: SPIE Advanced Lithography, 2019, 10961: 109610N

[29]

Watanabe Y, Kimura T, Matsunawa T, Nojima S. Accurate lithography simulation model based on convolutional neural networks. In: Proceedings of the XXIV Symposium on Photomask and Next-Generation Lithography Mask Technology. Yokohama: SPIE, 2017, 10147: 104540I

[30]

Sun R, Kang D, Jia C, Liu M, Shao D, Kim Y, Shin J, Mark S, Zhao Q, Feng M, Zhao Y, Wang S, Kim S, Ko S, Kim S, Choi J, Park C. Enhancing model accuracy and calibration efficiency with image-based pattern selection using machine learning techniques. In: Optical Microlithography XXXIV. San Jose: SPIE Advanced Lithography, 2021, 11613: 116130Y

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (2955KB)

2318

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/