output
20052026
most citedHigh-resolution remote thermography using luminescent low-dimensional tin-halide perovskites

367 citations

78 papers

physics.optics2026

Comparison between methods to measureintensity modulator Vpi

Gilles Feugnet, Salma Essawab, Jimmy Pennanech +6

We describe and compare in this paper two common methods to measure the half-wave voltage (Vpi) of an electro-optical modulator versus RF frequency of the driving electronic signal…

physics.optics2025

Mid-infrared continua via spectral broadening and difference frequency generation in a nanophotonic lithium niobate waveguide

Markus Ludwig, Furkan Ayhan, Thibault Voumard +5

Periodically poled thin film lithium niobate waveguides provide simultaneous access to efficient second and third order nonlinear processes, enabling broadband generation of cohere…

cs.LG2025★ 1 cited

SolarCrossFormer: Improving day-ahead Solar Irradiance Forecasting by Integrating Satellite Imagery and Ground Sensors

Baptiste Schubnel, Jelena Simeunović, Corentin Tissier +2

Accurate day-ahead forecasts of solar irradiance are required for the large-scale integration of solar photovoltaic (PV) systems into the power grid. However, current forecasting s…

physics.optics2024★ 15 cited

Ultrabroadband tunable difference frequency generation in standardized thin-film lithium niobate platform

Yesim Koyaz, Christian Lafforgue, Homa Zarebidaki +4

Thin-film lithium niobate (TFLN) on insulator is a promising platform for nonlinear photonic integrated circuits (PICs) due to its strong light confinement, high second-order nonli…

quant-ph2024★ 6 cited

Single-photon detectors on arbitrary photonic substrates

Max Tao, Hugo Larocque, Samuel Gyger +16

Detecting non-classical light is a central requirement for photonics-based quantum technologies. Unrivaled high efficiencies and low dark counts have positioned superconducting nan…

cs.CV2024★ 1 cited

PriPHiT: Privacy-Preserving Hierarchical Training of Deep Neural Networks

Yamin Sepehri, Pedram Pad, Pascal Frossard +1

The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training d…