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Three-Dimensional Electron Energy Deposition Modeling of Cathodoluminescence Emission near Threading Dislocations in GaN and Electron-Beam Lithography Exposure Parameters for a PMMA Resist

Published online by Cambridge University Press:  12 November 2012

Hendrix Demers ,
Nicolas Poirier-Demers ,
Matthew R. Phillips ,
Niels de Jonge  and
Dominique Drouin
Hendrix Demers
Affiliation:
Universite de Sherbrooke, Electrical and Computer Engineering Department, Sherbrooke, Quebec J1K 2R1, Canada
Nicolas Poirier-Demers
Affiliation:
Universite de Sherbrooke, Electrical and Computer Engineering Department, Sherbrooke, Quebec J1K 2R1, Canada
Matthew R. Phillips
Affiliation:
University of Technology, Microstructural Analysis Unit, Faculty of Science, Sydney, NSW 2007, Australia
Niels de Jonge
Affiliation:
Vanderbilt UniversitySchool of Medicine, Department of Molecular Physiology and Biophysics, Nashville, TN 37232-0615, USA
Dominique Drouin*
Affiliation:
Universite de Sherbrooke, Electrical and Computer Engineering Department, Sherbrooke, Quebec J1K 2R1, Canada
*
*Corresponding author. E-mail: [email protected]
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Abstract

The Monte Carlo software CASINO has been expanded with new modules for the simulation of complex beam scanning patterns, for the simulation of cathodoluminescence (CL), and for the calculation of electron energy deposition in subregions of a three-dimensional (3D) volume. Two examples are presented of the application of these new capabilities of CASINO. First, the CL emission near threading dislocations in gallium nitride (GaN) was modeled. The CL emission simulation of threading dislocations in GaN demonstrated that a better signal-to-noise ratio was obtained with lower incident electron energy than with higher energy. Second, the capability to simulate the distribution of the deposited energy in 3D was used to determine exposure parameters for polymethylmethacrylate resist using electron-beam lithography (EBL). The energy deposition dose in the resist was compared for two different multibeam EBL schemes by changing the incident electron energy.

Keywords

cathodoluminescence (CL)electron-beam lithographyenergy depositionMonte Carlo simulationsresistthreading dislocationthree-dimensional (3D)
Type
Special Section: Cathodoluminescence
Information
Microscopy and Microanalysis , Volume 18 , Issue 6 , December 2012 , pp. 1220 - 1228
Copyright
Copyright © Microscopy Society of America 2012

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Footnotes

†

Current address: INM Leibniz-Institute for New Materials, Campus D2 2, 66123 Saarbrücken, Germany

References

Adesida, I., Everhart, T.E. & Shimizu, R. (1979). High resolution electron-beam lithography on thin films. J Vac Sci Technol 16(6), 1743–1748.Google Scholar
Aktary, M., Stepanova, M. & Dew, S.K. (2006). Simulation of the spatial distribution and molecular weight of polymethylmethacrylate fragments in electron beam lithography exposures. J Vac Sci Technol B 24(2), 768–779.Google Scholar
Babin, S., Borisov, S., Ivanchikov, A. & Ruzavin, I. (2006). Modeling of linewidth measurement in SEMs using advanced Monte Carlo software. J Vac Sci Technol B 24(6), 3121–3124.Google Scholar
Barjon, J., Brault, J., Daudin, B., Jalabert, D. & Sieber, B. (2003). Cathodoluminescence study of carrier diffusion in AlGaN. J Appl Phys 94(4), 2755–2757.CrossRefGoogle Scholar
Bishop, H.E. (1965). A Monte Carlo calculation on the scattering of electrons in copper. Proc Phys Soc 85(5), 855–866.CrossRefGoogle Scholar
Chen, W. & Ahmed, H. (1997). Nanofabrication for electronics. In Advances in Imaging and Electron Physics, Hawkes, P.W. (Ed.), pp. 87–185. New York: Academic Press.Google Scholar
de Jonge, N., Poirier-Demers, N., Demers, H., Peckys, D.B. & Drouin, D. (2010). Nanometer-resolution electron microscopy through micrometers-thick water layers. Ultramicroscopy 110(9), 1114–1119.Google Scholar
Demers, H., Poirier-Demers, N., Couture, A.R., Joly, D., Guilmain, M., de Jonge, N. & Drouin, D. (2011). Three-dimensional electron microscopy simulation with the CASINO Monte Carlo software. Scanning 33(3), 135–146.Google Scholar
Drouin, D., Couture, A.R., Joly, D., Tastet, X., Aimez, V. & Gauvin, R. (2007). CASINO V2.42—A fast and easy-to-use modeling tool for scanning electron microscopy and microanalysis users. Scanning 29(3), 92–101.Google Scholar
Drouin, D., Hovington, P. & Gauvin, R. (1997). CASINO: A new Monte Carlo code in C language for electron beam interaction—Part II: Tabulated values of Mott cross section. Scanning 19(1), 20–28.Google Scholar
Fleischer, K., Toth, M., Phillips, M.R., Zou, J., Li, G. & Chua, S.J. (1999). Depth profiling of GaN by cathodoluminescence microanalysis. J Appl Phys 74(8), 1114–1116.Google Scholar
Gauvin, R. & L'Espérance, G. (1992). A Monte Carlo code to simulate the effect of fast secondary electron on k AB factors and spatial resolution in the TEM. J Microsc 168(2), 153–167.CrossRefGoogle Scholar
Gelhausen, O., Phillips, M.R. & Toth, M. (2001). Depth-resolved cathodoluminescence microanalysis of near-edge emission in III-nitride thin films. J Appl Phys 89(6), 3535–3537.CrossRefGoogle Scholar
Glezos, N. & Raptis, I. (1996). A fast electron beam lithography simulator based on the Boltzmann transport equation. IEEE T Comput Aid Design Int Circ Syst 15(1), 92–102.CrossRefGoogle Scholar
Glezos, N., Raptis, I., Tsoukalas, D. & Hatzakis, M. (1992). Application of a new analytical technique of electron distribution calculations to the profile simulation of a high sensitivity negative electron-beam resist. J Vac Sci Technol B 10(6), 2606–2609.Google Scholar
Greeneich, J.S. & Duzer, T.V. (1974). An exposure model for electron-sensitive resists. IEEE T Electron Dev 21(5), 286–299.Google Scholar
Han, L., McCord, M.A., Winograd, G.I. & Pease, R.F.W. (1998). Performance investigation of Coulomb interaction-limited high throughput electron beam lithography based on empirical modeling. J Vac Sci Technol B 16(6), 3215–3220.Google Scholar
Holt, D.B. & Napchan, E. (1994). Quantification of SEM EBIC and CL signal using Monte Carlo electron-trajectory simulations. Scanning 16(2), 78–86.Google Scholar
Hovington, P., Drouin, D. & Gauvin, R. (1997a). CASINO: A new Monte Carlo code in C language for electron beam interaction—Part I: Description of the program. Scanning 19(1), 1–14.Google Scholar
Hovington, P., Drouin, D., Gauvin, R., Joy, D.C. & Evans, N. (1997b). CASINO: A new Monte Carlo code in C language for electron beam interaction—Part III: Stopping power at low energies. Scanning 19(1), 29–35.Google Scholar
Icard, B., Rio, D., Veltman, P., Kampherbeek, B., Constancias, C. & Pain, L. (2009). Development of resist process for 5-KV multi-beam technology. In Proc SPIE 7271, Schellenberg, F.M. & La Fontaine, B.M. (Eds.), pp. 72710R. San Jose, CA.Google Scholar
Joy, D.C. (1995). Monte Carlo Modeling for Electron Microscopy and Microanalysis. New York: Oxford University Press.CrossRefGoogle Scholar
Joy, D.C. & Luo, S. (1989). An empirical stopping power relationship for low-energy electrons. Scanning 11(4), 176–180.CrossRefGoogle Scholar
Kanaya, K. & Okayama, S. (1972). Penetration and energy-loss theory of electrons in solid targets. J Phys D Appl Phys 5(1), 43–58.CrossRefGoogle Scholar
Kim, S.-H., Ham, Y.-M., Lee, W. & Chun, K. (1998). New approach of Monte Carlo simulation for low energy electron beam lithography. Microelectron Eng 41–42, 179–182.Google Scholar
Kyser, D.F. & Viswanathan, N.S. (1975). Monte Carlo simulation of spatially distributed beams in electron-beam lithography. J Vac Sci Technol 12(6), 1305–1308.Google Scholar
Liu, E.D. & Prescop, T. (2011). Optimization of e-beam landing energy for EBDW. In Proc SPIE 7970, Herr, D.J.C. (Ed.), pp. 79701S. San Jose, CA.Google Scholar
McCarthy, L., Smorchkova, I., Xing, H., Fini, P., Keller, S., Speck, J., DenBaars, S.P., Rodwell, M.J.W. & Mishra, U.K. (2001). Effect of threading dislocations on AlGaN/GaN heterojunction bipolar transistors. Appl Phys Lett 78(15), 2235–2237.Google Scholar
Murata, K., Kawata, H., Nagami, K., Hirai, Y. & Mano, Y. (1987). Studies of energy dissipation in resist films by a Monte Carlo simulation based on the Mott cross section. J Vac Sci Technol B 5(1), 124–128.CrossRefGoogle Scholar
Murata, K., Kyser, D.F. & Ting, C.H. (1981). Monte Carlo simulation of fast secondary electron production in electron beam resists. J Appl Phys 52(7), 4396–4405.Google Scholar
Newbury, D.E. & Myklebust, R.L. (1981). A Monte Carlo electron trajectory simulation for analytical electron microscopy. In Analytical Electron Microscopy, Geiss, R.H. (Ed.), pp. 91–98. San Francisco, CA: San Francisco Press.Google Scholar
Nouiri, A. & Aouati, R. (2008). Monte Carlo model of cathodoluminescence characterization of AlAs/GaAs/AlAs laser diode. Physica E 40(5), 1751–1753.Google Scholar
Pauc, N., Phillips, M.R., Aimez, V. & Drouin, D. (2006). Carrier recombination near threading dislocations in GaN epilayers by low voltage cathodoluminescence. Appl Phys Lett 89(16), 161905. Google Scholar
Pease, R.F. & Chou, S.Y. (2008). Lithography and other patterning techniques for future electronics. Proc IEEE 96(2), 248–270.Google Scholar
Raptis, I., Glezos, N. & Hatzakis, M. (1993). Analytical evaluation of the energy deposition function in electron-beam lithography in the case of a composite substrate. Proc 16th Int Symp Electron Ion 11(6), 2754–2757.Google Scholar
Reimer, L. (1998). Scanning Electron Microscopy: Physics of Image Formation and Microanalysis. Berlin: Springer.Google Scholar
Rose, A. (1948). Television pickup tubes and the problem of vision. In Advances in Electronics and Electron Physics, Marton, L. (Ed.), pp. 131–166. New York: Academic Press.Google Scholar
Schleunitz, A., Spreu, C., Vogler, M., Atasoy, H. & Schift, H. (2011). Combining nanoimprint lithography and a molecular weight selective thermal reflow for the generation of mixed 3D structures. J Vac Sci Technol B 29(6), 06FC01. Google Scholar
Stepanova, M., Fito, T., Szabó, Z., Alti, K., Adeyenuwo, A.P., Koshelev, K., Aktary, M. & Dew, S.K. (2010). Simulation of electron beam lithography of nanostructures. J Vac Sci Technol B 28(6), C6C48–C46C57.Google Scholar
Stoliarov, S.I., Westmoreland, P.R., Nyden, M.R. & Forney, G.P. (2003). A reactive molecular dynamics model of thermal decomposition in polymers: I. Poly(methyl methacrylate). Polymer 44(3), 883–894.Google Scholar
Toth, M. (2006). Microcharacterization of GaN defect structure. PhD Thesis. Sydney, Australia: University of Technology. Google Scholar
Toth, M. & Phillips, M.R. (1998). Monte Carlo modeling of cathodoluminescence generation using electron energy loss curves. Scanning 20(6), 425–432.Google Scholar
Zhou, J. & Yang, X. (2006). Monte Carlo simulation of process parameters in electron beam lithography for thick resist patterning. J Vac Sci Technol B 24(3), 1202–1209.Google Scholar