Published April 11, 2024 | Version v1
Journal article

Suppressing electromagnetic local density of states via slow light in lossy quasi-one-dimensional gratings

  • 1. Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey 08544, USA
  • 2. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
  • 3. Department of Engineering Physics, Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada

Description

We propose a spectral-averaging procedure that enables the computation of bandwidth-integrated local density of states (LDOS) from a single scattering calculation, and exploit it to investigate the minimum extinction achievable from dipolar sources over nonzero bandwidths in structured media. Structure-agnostic extinction bounds are derived, providing analytical insights into scaling laws and fundamental design tradeoffs with implications to bandwidth and material selection. We find that perfect LDOS suppression over a nonzero bandwidth Δω is impossible. Inspired by limits which predict nontrivial Δω scaling in systems with material dissipation, we show that the pseudogap edge states of quasi-one-dimensional bullseye gratings can—by simultaneously minimizing material absorption and radiation—yield arbitrarily close to perfect LDOS suppression in the limit of vanishing bandwidth.

Additional details

Identifiers

DOI
10.1103/PhysRevA.109.L041501;
arXiv
arXiv:2309.15794;
Crossref Funder ID
10.13039/100008585; 10.13039/100000010; 10.13039/501100010785; 10.13039/501100019217; 10.13039/100006734;

Publishing Information

Journal Title
Physical Review A
Journal Volume
109
Journal Issue
4
Journal Page Range
6 pgs.
ISSN
1094-1622

Optional Information

Copyright
©2024 American Physical Society
Contract/Grant/Project number
DMR-1719875
Notes
These authors contributed equally to this work.; Record automatically processed
Funding organization
Cornell Center for Materials Research; Ford Foundation; Canada First Research Excellence Fund; Institut de Valorisation des Données; Princeton University