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Nanoplasmonic Metal–Insulator–Metal Waveguides

  • Moshik CohenEmail author
  • Reuven Shavit
  • Zeev Zalevsky
Chapter
Part of the Springer Series in Optical Sciences book series (SSOS, volume 189)

Abstract

The exceptional ability of plasmonic structures to confine light into deep subwavelength volumes has fashioned rapid expansion of interest from both fundamental and applicative perspectives. Surface plasmon nanophotonics enables to investigate light–matter interaction in deep nanoscale and harness the electromagnetic and quantum properties of materials, thus opening pathways of tremendous potential applications. Predominantly, metal–insulator–metal (MIM) plasmonic waveguides are of special attentiveness as they enable to confine and manipulate light in deep nanometer scale. This work includes two sections with state-of-the-art work in the field of MIM nanoplasmonic waveguides. The first section describes novel engineerable interferometry architecture with extremely compact dimensions of λ3/15,500, which can be used to realize a variety of plasmonic logic functionalities. We use this architecture to realize the smallest reported plasmonic XOR logic gate. In the second section we use Kelvin probe force microscopy (KPFM) under optical illumination to image plasmonic waves, achieving spatial resolution of 2 nm. We fabricate a series of plasmonic MIM waveguides with gap width varied by 2 nm and experimentally resolve their propagation properties. By comparing experimentally obtained images with theoretical calculation results, we show that KPFM maps provide valuable information on the direction of optical near field. Additionally, we propose a theoretical model for the relation between surface plasmons and the material work function measured by KPFM.

Keywords

Extinction Ratio Plasmonic Waveguide Contact Potential Difference Kelvin Probe Force Microscopy Surface Electric Potential 
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.

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© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Faculty of EngineeringBar-Ilan UniversityRamat-GanIsrael
  2. 2.Department of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael
  3. 3.Bar-Ilan Institute for Nanotechnology and Advanced MaterialsRamat-GanIsrael

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