Tuning the Ultrafast Response of Fano Resonances in Halide Perovskite Nanoparticles
Paolo Franceschini, Luca Carletti, Anatoly P. Pushkarev, Fabrizio, Preda, Antonio Perri, Andrea Tognazzi, Andrea Ronchi, Gabriele Ferrini,, Stefania Pagliara, Francesco Banfi, Dario Polli, Giulio Cerullo, Costantino, De Angelis, Sergey V. Makarov, Claudio Giannetti

TL;DR
This paper demonstrates how combining geometrical design and ultrafast photoinjection in halide perovskite nanoparticles enables tunable, ultrafast control of Fano resonances, with potential applications in nano-antennas and optical switches.
Contribution
It introduces a method to ultrafastly modulate Fano resonances in perovskite nanoparticles through geometry and carrier injection, revealing reversed modulation behavior.
Findings
Ultrafast carrier injection modifies optical properties within sub-picoseconds.
Resonance modulation can be reversed by tuning nanoparticle size.
Interplay of chemical, geometrical, and ultrafast control enhances nano-photonic device capabilities.
Abstract
The full control of the fundamental photophysics of nanosystems at frequencies as high as few THz is key for tunable and ultrafast nano-photonic devices and metamaterials. Here we combine geometrical and ultrafast control of the optical properties of halide perovskite nanoparticles, which constitute a prominent platform for nanophotonics. The pulsed photoinjection of free carriers across the semiconducting gap leads to a sub-picosecond modification of the far-field electromagnetic properties that is fully controlled by the geometry of the system. When the nanoparticle size is tuned so as to achieve the overlap between the narrowband excitons and the geometry-controlled Mie resonances, the ultrafast modulation of the transmittivity is completely reversed with respect to what is usually observed in nanoparticles with different sizes, in bulk systems and in thin films. The interplay…
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.
