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Applied Physics B

, 122:199 | Cite as

Determination of local optical response functions of nanostructures with increasing complexity by using single and coupled Lorentzian oscillator models

  • Martin Aeschlimann
  • Tobias Brixner
  • Alexander Fischer
  • Matthias Hensen
  • Bernhard Huber
  • Deirdre Kilbane
  • Christian Kramer
  • Walter Pfeiffer
  • Martin Piecuch
  • Philip Thielen
Article
Part of the following topical collections:
  1. Ultrafast Nanooptics

Abstract

We reconstruct the optical response of nanostructures of increasing complexity by fitting interferometric time-resolved photoemission electron microscopy (PEEM) data from an ultrashort (21 fs) laser excitation source with different harmonic oscillator-based models. Due to its high spatial resolution of ~40 nm, PEEM is a true near-field imaging system and enables in normal incidence mode a mapping of plasmon polaritons and an intuitive interpretation of the plasmonic behaviour. Using an actively stabilized Mach–Zehnder interferometer, we record two-pulse correlation signals with 50 as time resolution that contain information about the temporal plasmon polariton evolution. Spectral amplitude and phase of excited plasmon polaritons are extracted from the recorded phase-resolved interferometric two-pulse correlation traces. We show that the optical response of a plasmon polariton generated at a gold nanoparticle can be reconstructed from the interferometric two-pulse correlation signal using a single harmonic oscillator model. In contrast, for a corrugated silver surface, a system with increased plasmonic complexity, in general an unambiguous reconstruction of the local optical response based on coupled and uncoupled harmonic oscillators, fails. Whereas for certain local responses different models can be discriminated, this is impossible for other positions. Multidimensional spectroscopy offers a possibility to overcome this limitation.

Keywords

Electron Energy Loss Spectroscopy Photoemission Process PEEM Image Photoemission Electron Microscopy Model Response Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

All authors of this work are listed in alphabetical order. This work was supported by the German Science Foundation (DFG) within the SPP 1391 (M.A., T.B., W.P.) and the GSC 266 (P.T.). D.K. acknowledges funding from the Irish Research Council and the Marie Curie Actions ELEVATE fellowship.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Martin Aeschlimann
    • 1
  • Tobias Brixner
    • 2
    • 3
  • Alexander Fischer
    • 1
    • 6
  • Matthias Hensen
    • 2
    • 4
  • Bernhard Huber
    • 2
  • Deirdre Kilbane
    • 1
  • Christian Kramer
    • 2
  • Walter Pfeiffer
    • 4
  • Martin Piecuch
    • 1
  • Philip Thielen
    • 1
    • 5
  1. 1.Fachbereich Physik and Research Center OPTIMASTechnische Universität KaiserslauternKaiserslauternGermany
  2. 2.Institut für Physikalische und Theoretische ChemieUniversität WürzburgWürzburgGermany
  3. 3.Röntgen Research Center for Complex Material Systems (RCCM)Universität WürzburgWürzburgGermany
  4. 4.Fakultät für PhysikUniversität BielefeldBielefeldGermany
  5. 5.Graduate School of Excellence Materials Science in MainzKaiserslauternGermany
  6. 6.Deutsches Zentrum für Luft- und Raumfahrt (DLR)Institut für Technische Physik, FestkörperlaserStuttgartGermany

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