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Towards Next Generation of Optoelectronics: from Quantum Plasmonics and 2D Materials to Advanced Optimization Techniques of Nanophotonic Devices


Tokpanov, Yury (2020) Towards Next Generation of Optoelectronics: from Quantum Plasmonics and 2D Materials to Advanced Optimization Techniques of Nanophotonic Devices. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/tg1b-hn35.


In this thesis, we explore different novel concepts and materials for the next-generation of nanophotonic and optoelectronic devices that could be used both in classical and quantum settings.

First, we study quantum coherence properties of surface plasmon polaritons (SPPs) in the regime of extreme dispersion. Most experiments to date, that tested quantum coherence properties of SPPs, used essentially weakly-confined plasmons, which experience limited light-matter hybridization, thus restricting the potential for decoherence. Our setup is based on a hole-array chip supporting SPPs near the surface plasma frequency, where plasmonic dispersion and confinement is much stronger than in previous experiments, making the plasmons much more susceptible for decoherence processes. We generated polarization-entangled pairs of photons and transmitted one of the photons through this plasmonic hole array. Our results show that the quality of photon entanglement after the highly-dispersive plasmonic channel is unperturbed. Our findings provide a lower bound of 100 femtoseconds for the pure dephasing time of dispersive plasmons in our materials, and show that even in a highly dispersive regime, surface plasmons preserve quantum mechanical correlations, making possible harnessing the power of extreme light confinement for integrated quantum photonics.

Second, we systematically study different passivation schemes of sulfur vacancies in 2D molybdenum disulfide using first-principles calculations based on density functional theory. We aim at building a microscopic understanding of passivation mechanisms of treatment with TFSI superacid - a popular approach of to improve optical properties. Since superacids have a strong ability to donate protons, we consider hydrogenation and protonation of sulfur vacancies as a possible passivation scheme. Our calculations show that effects of protonation and hydrogenation on properties of 2D molybdenum disulfide are very similar. Moreover, we find that four hydrogen atoms can fully "heal" sulfur vacancies in this material. Our results are an important step towards controllable defects design in 2D transition metal dichalcogenides.

And third, we study applications of advanced methods of optimization and machine learning to the design of different nanophotonic devices. We explore feasibility of using novel multi-fidelity Gaussian processes optimization technique to optimize plasmonic mirror filters for hyperspectral imaging. We compare our results with other common optimization approaches. Then we apply deep-learning inspired techniques to optimize control voltages of individual pixels of active metasurfaces to achieve dynamic beamsteering. We obtain interesting results that pave the way for future experiments both in nanophotonics and machine learning fields.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:nanophotonics, quantum plasmonics, 2D materials, first-principle calculations, machine learning, optimization
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Applied Physics
Minor Option:Computer Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Atwater, Harry Albert
Thesis Committee:
  • Faraon, Andrei (chair)
  • Vahala, Kerry J.
  • Atwater, Harry Albert
  • Yue, Yisong
Defense Date:21 May 2020
Record Number:CaltechThesis:06012020-093627645
Persistent URL:
Related URLs:
URLURL TypeDescription 10.1103/PhysRevApplied.12.044037DOIArticle adapted for ch. 2 adapted for ch. 4
Tokpanov, Yury0000-0001-5123-7428
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13755
Deposited By: Yury Tokpanov
Deposited On:03 Jun 2020 17:40
Last Modified:08 Nov 2023 00:12

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