Orders-of-magnitude reduction in photonic mode volume by nano-sculpting
Rasmus E. Christiansen, Jesper M{\o}rk, Ole Sigmund

TL;DR
This paper demonstrates that nano-sculpting dielectric nanostructures can achieve extremely small optical mode volumes and potentially very high quality factors, greatly enhancing light-matter interaction for photonic applications.
Contribution
The study introduces topology optimization to design dielectric nanostructures with unprecedented spatial light confinement and explores how encapsulation with ellipsoidal shells can vastly increase the quality factor.
Findings
Mode volumes are reduced by three to four orders of magnitude below the diffraction limit.
Encapsulation with ellipsoidal shells can lead to quality factors exceeding 10^8.
The Purcell factor can be enhanced above 10^11.
Abstract
Achieving strong light-matter interaction is important for studying and exploiting several physics phenomena. The light-matter interaction strength depends on the optical field intensity in the interaction region, often measured by the Purcell factor, which for a single emitter is proportional to the spectral confinement, quantified by the cavity quality factor , and inversely proportional to the spatial localization of light, quantified by the optical model volume , . While plasmonic (metallic) devices can support extreme spatial light confinement, ohmic losses reduce the cavity lifetime, thereby limiting the achievable spectral confinement. It is therefore of both practical and fundamental interest to explore the potential for achieving extreme spatial light confinement in (near) loss-less dielectric environments. Employing topology optimization we explore…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPhotonic and Optical Devices · Advanced Fiber Laser Technologies · Neural Networks and Reservoir Computing
