Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-23T17:47:11.178Z Has data issue: false hasContentIssue false
  • English
  • Français

High-Resolution Cathodoluminescence Hyperspectral Imaging of Nitride Nanostructures

Published online by Cambridge University Press:  05 December 2012

Paul R. Edwards ,
Lethy Krishnan Jagadamma ,
Jochen Bruckbauer ,
Chaowang Liu ,
Philip Shields ,
Duncan Allsopp ,
Tao Wang  and
Robert W. Martin
Paul R. Edwards*
Affiliation:
Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
Lethy Krishnan Jagadamma
Affiliation:
Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
Jochen Bruckbauer
Affiliation:
Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
Chaowang Liu
Affiliation:
Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom
Philip Shields
Affiliation:
Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom
Duncan Allsopp
Affiliation:
Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom
Tao Wang
Affiliation:
EPSRC National Centre for III-V Technologies, Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, United Kingdom
Robert W. Martin
Affiliation:
Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
*
*Corresponding author. E-mail: paul.edwards@strath.ac.uk
Get access

Abstract

Hyperspectral cathodoluminescence imaging provides spectrally and spatially resolved information on luminescent materials within a single dataset. Pushing the technique toward its ultimate nanoscale spatial limit, while at the same time spectrally dispersing the collected light before detection, increases the challenge of generating low-noise images. This article describes aspects of the instrumentation, and in particular data treatment methods, which address this problem. The methods are demonstrated by applying them to the analysis of nanoscale defect features and fabricated nanostructures in III-nitride-based materials.

Keywords

cathodoluminescencehyperspectral imaginggallium nitrideprincipal component analysismultivariate statistical analysisSEM
Type
Special Section: Cathodoluminescence
Information
Microscopy and Microanalysis , Volume 18 , Issue 6 , December 2012 , pp. 1212 - 1219
Copyright
Copyright © Microscopy Society of America 2012

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

Bruckbauer, J., Edwards, P.R., Wang, T. & Martin, R.W. (2011). High resolution cathodoluminescence hyperspectral imaging of surface features in InGaN/GaN multiple quantum well structures. Appl Phys Lett 98, 141908. Google Scholar
Christen, J., Grundmann, M. & Bimberg, D. (1991). Scanning cathodoluminescence microscopy—A unique approach to atomic-scale characterization of heterointerfaces and imaging of semiconductor inhomogeneities. J Vac Sci Technol B 9, 2358–2368.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, 92–101.Google Scholar
Edwards, P.R. & Martin, R.W. (2011). Cathodoluminescence nano-characterization of semiconductors. Semicond Sci Technol 26, 064005. Google Scholar
Edwards, P.R., Martin, R.W. & Lee, M.R. (2007). Combined cathodoluminescence hyperspectral imaging and wavelength dispersive X-ray analysis of minerals. Am Miner 92, 235–242.CrossRefGoogle Scholar
Edwards, P.R., Sleith, D., Wark, A.W. & Martin, R.W. (2011). Mapping localized surface plasmons within silver nanocubes using cathodoluminescence hyperspectral imaging. J Phys Chem C 115, 14031–14035.Google Scholar
Guiton, B.S., Iberi, V., Li, S., Leonard, D.N., Parish, C.M., Kotula, P.G., Varela, M., Schatz, G.C., Pennycook, S.J. & Camden, J.P. (2011). Correlated optical measurements and plasmon mapping of silver nanorods. Nano Lett 11, 3482–3488.Google Scholar
Keenan, M.R. (2009). Exploiting spatial-domain simplicity in spectral image analysis. Surf Interface Anal 41, 79–87.Google Scholar
Kotula, P.G., Keenan, M.R. & Michael, J.R. (2003). Automated analysis of SEM X-ray spectral images: A powerful new microanalysis tool. Microsc Microanal 9, 1–17.CrossRefGoogle ScholarPubMed
Liu, C., Šatka, A., Jagadamma, L.K., Edwards, P.R., Allsopp, D., Martin, R.W., Shields, P., Kovac, J., Uherek, F. & Wang, W. (2009). Light emission from InGaN quantum wells grown on the facets of closely spaced GaN nano-pyramids formed by nano-imprinting. Appl Phys Express 2, 121002. Google Scholar
Norman, C. E. (2000). Challenging the spatial resolution limits of CL and EBIC. Solid State Phenom 78–79, 19–25.Google Scholar
Press, W.H., Teukolsky, S.A., Vetterling, W.T. & Flannery, B.P. (2002). Moments of a distribution: Mean, variance, skewness, and so forth. In Numerical Recipes in C++: The Art of Scientific Computing, 2nd ed., Chap. 14.1, pp. 615–620. Cambridge, UK: Cambridge University Press.Google Scholar