Hostname: page-component-76fb5796d-skm99 Total loading time: 0 Render date: 2024-04-25T08:05:08.876Z Has data issue: false hasContentIssue false

Monte Carlo Simulation on the CD-SEM Images of SiO2/Si Systems

Published online by Cambridge University Press:  03 May 2019

P. Zhang*
Affiliation:
School of Electronic Information Engineering, Yangtze Normal University, Chongqing 408100, China
*
Author for correspondence: P. Zhang, E-mail: zhangp007@foxmail.com; zhangp007@mail.ustc.edu.cn
Get access

Abstract

Silicon dioxide (SiO2) has been the most important insulator in the highly-developed field of silicon (Si) technology. Accurate pitch and gate linewidth measurements for SiO2/Si systems (systems with a SiO2 insulating layer and Si substrate) have become necessary. Studying one such system obviously presents different results from that of the widely researched Si/Si structure, because the edge profile of the secondary electron (SE) signal contains contributions from two materials. In this work, several scanning electron microscope (SEM) images and SE profiles of SiO2/Si pitch and trapezoidal line structures, using various geometric and experimental parameters, were simulated through the use of Monte Carlo (MC) methods. It was found that, in contrast to Si/Si systems, the height of the insulating layer cannot be ignored during the evaluation of pitch and linewidth. The thickness (i.e., height) factor does play an important role in the contrast of SEM imaging and the shape of the SE profile in these two-material systems. The mechanism of the influence of insulating layer thickness for imaging was studied in detail. In addition, the SiO2/Si pitch structure with a real rough surface was also studied. This work has significant implications for the study of various kinds of two-material systems and could help to optimize the pitch and gate linewidth measurements.

Type
Materials Applications
Copyright
Copyright © Microscopy Society of America 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abe, H, Babin, S, Borisov, S, Hamaguchi, A, Kadowaki, M, Miyano, Y & Yamazaki, Y (2009). Contrast reversal effect in scanning electron microscopy due to charginga). J Vacuum Sci Technol B Microelectron Nanomet Struct 27(3), 10391042.Google Scholar
Bonham, RA & Strand, TG (1963). Analytical expressions for potentials of neutral Thomas—Fermi—Dirac atoms and for the corresponding atomic scattering factors for X rays and electrons. J Chem Phys 39(9), 22002204.Google Scholar
Ciappa, M, Ilgünsatiroglu, E & Illarionov, AY (2015). Extraction of roughness parameters at nanometer scale by Monte Carlo simulation of critical dimension scanning electron microscopy. Solid-State Electron 113, 7378.Google Scholar
Ding, ZJ & Li, HM (2010). Application of Monte Carlo simulation to SEM image contrast of complex structures. Surf Interface Anal 37(11), 912918.Google Scholar
Ding, ZJ, Li, HM, Goto, K, Jiang, YZ & Shimizu, R (2004). Energy spectra of backscattered electrons in Auger electron spectroscopy: Comparison of Monte Carlo simulation with experiment. J Appl Phys 96(8), 45984606.Google Scholar
Ding, ZJ & Shimizu, R (1988). Monte Carlo study of backscattering and secondary electron generation. Surf Sci 197(3), 539554.Google Scholar
Ding, ZJ, Tang, XD & Shimizu, R (2001). Monte Carlo study of secondary electron emission. J Appl Phys 89(1), 718726.Google Scholar
Fijol, JJ, Then, MA, Tasker, WG & J, R (1991). Secondary electron yield of SiO2 and Si3N4 thin films for continuous dynode electron multipliers. Appl Surf Sci 48–49(91), 464471.Google Scholar
Frase, CG, Gnieser, D & Bosse, H (2009). Model-based SEM for dimensional metrology tasks in semiconductor and mask industry. J Phys D Appl Phys 42(42), 183001.Google Scholar
Gorelikov, DV, Remillard, J, Sullivan, NT & Davidson, M (2010). Model-based CD–SEM metrology at low and ultralow landing energies: Implementation and results for advanced IC manufacturing. Surf Interface Anal 37(11), 959965.Google Scholar
Hoefflinger, B (2011). ITRS: The International Technology Roadmap for Semiconductors, Springer Berlin Heidelberg.Google Scholar
Hovington, P, Drouin, D & Gauvin, R (2010). CASINO: A new Monte Carlo code in C language for electron beam interaction––part I: Description of the program. Scanning 19(1), 2028.Google Scholar
Joy, DC (2010). A database on electron-solid interactions. Scanning 17(5), 270275.Google Scholar
Li, C, Mao, S, Zou, Y, Li, Y, Zhang, P, Li, H & Ding, ZJ (2018). A Monte Carlo modeling on charging effect for structures with arbitrary geometries. J Phys D Appl Phys 51(16), 165301-1-21.Google Scholar
Li, HM & Ding, ZJ (2005). A Monte Carlo simulation of secondary electron and backscattered electron images in scanning electron microscopy. Acta Metallurgica Sinica(English Letters) 18(3), 351355.Google Scholar
Li, YG, Mao, SF, Li, HM, Xiao, SM & Ding, ZJ (2008). Monte Carlo simulation study of scanning electron microscopy images of rough surfaces. J Appl Phys 104(6), 25.Google Scholar
Li, YG, Zhang, P & Ding, ZJ (2013). Monte Carlo simulation of CD-SEM images for linewidth and critical dimension metrology. Scanning 35(2), 127139.Google Scholar
Lowney, JR (1995). Use of Monte Carlo modeling for interpreting scanning electron microscope linewidth measurements. Scanning 17(5), 281286.Google Scholar
Lowney, JR (2010). Monte Carlo simulation of scanning electron microscope signals for lithographic metrology. Scanning 18(4), 301306.Google Scholar
Mott, NF (1929). The scattering of fast electrons by atomic nuclei. Proc R Soc Lond 124(794), 425442.Google Scholar
Palik, ED (1991). Handbook of optical constants of solids II. Boston Acad Press 1(1), 77135.Google Scholar
Penn, DR (1987). Electron mean-free-path calculations using a model dielectric function. Phys Rev B Condens Matter 35(2), 482.Google Scholar
Powell, CJ & Jablonski, A (2002). The NIST electron effective-attenuation-length database. J Surf Anal 9(3), 322325.Google Scholar
Ruan, Z, Zhang, P, Mao, SF, Li, HM & Ding, ZJ (2014). Monte Carlo study of the influence of electron beam focusing to SEM image sharpness measurement. e-J Surf Sci Nanotechnol 12(17), 247251.Google Scholar
Seeger, A, Duci, A & Haussecker, H (2010). Scanning electron microscope charging effect model for chromium/quartz photolithography masks. Scanning 28(3), 179186.Google Scholar
Tanaka, M (2006). A fast MAP-based super-resolution algorithm for general motion. Proc. SPIE-IS&T Electron Imag 6065.Google Scholar
Villarrubia, JS, Andrá, V, Aacute, SE & Postek, MT (2005). Simulation study of repeatability and bias in the critical dimension scanning electron microscope. J Microlithogr Microfabricat Microsyst 4(3), 033002.Google Scholar
Villarrubia, JS & Vladar, AE (2005). Influence of focus variation on linewidth measurements. Proc SPIE-Int Soc Opt Eng 5752, 144155.Google Scholar
Vladár, AE, Cizmar, P, Villarrubia, JS & Postek, MT (2012). Can we get 3D-CD metrology right? Metrology, Inspection, and Process Control for Microlithography XXVI.Google Scholar
Williams, EB (1962). Methods of Experimental Physics Vol. 3: Molecular physics, Academic PR.Google Scholar
Yong, YC, Thong, JTL & Phang, JCH (1998). Determination of secondary electron yield from insulators due to a low-kV electron beam. J Appl Phys 84(8), 45434548.Google Scholar
Zhang, P (2018). Exploring the influence of a focusing and Gaussian profile electron beam in SEM imaging through Monte Carlo simulation. Mosc Univ Phys Bull 73(1), 8994.Google Scholar
Zhang, P, Mao, S & Ding, Z (2015). Monte Carlo study of the effective electron beam shape in scanning electron microscopic imaging. Eur Phys J Appl Phys 69(3), 30703-p1-p8.Google Scholar
Zhang, P, Mao, SF, Zhang, ZM & Ding, ZJ (2013). Monte Carlo Study of the Influence of Electron Beam Focusing to SEM Linewidth Measurement. Scanning Microscopies 2013: Advanced Microscopy Technologies for Defense, Homeland Security, Forensic, Life, Environmental, and Industrial Sciences 8729.Google Scholar
Zhang, P, Wang, HY, Li, YG, Mao, SF & Ding, ZJ (2012). Monte Carlo simulation of secondary electron images for real sample structures in scanning electron microscopy. Scanning 34(3), 145150.Google Scholar
Zhang, ZM & Ding, ZJ (2013). Monte Carlo study of the influence of electron beam focusing to SEM linewidth measurement. SPIE Defense, Security, and Sensing.Google Scholar
Zou, YB, Zhang, P, Mao, SF & Ding, ZJ (2014). Model-Based library for critical dimension metrology by CD-SEM. Microsc Microanal 20, 67.Google Scholar