Optics (physics.optics)

Abstract: In the field of optics and nanophotonics, simulation of electromagnetic scattering plays a major role in the study of complex nanostructures and optical devices. The numerical analysis of scattering spectra, even for nanocavities with simple geometry, is associated with significant computational difficulties. However, when the system exhibits certain symmetries, it becomes possible to simplify the problem through the process of separation of variables, which leads to a decrease in its dimension. In this paper, we aim to provide a practical guide to a fast simulation of linear and non-linear scattering problems in COMSOL Multiphysics for axisymmetric objects including computation of scattering cross-section as well as its multipolar decomposition, optical forces, and second harmonic generation. We also accompany the provided guide with the ready-to-run COMSOL models.

Abstract: Optical frequency combs find many applications in metrology, frequency standards, communications and photonic devices. We consider field polarization properties and describe a vector comb generation through the spontaneous symmetry breaking of temporal cavity solitons within coherently driven, passive, Fabry-P\'erot cavities with Kerr nonlinearity. Global coupling effects due to the interactions of counter-propagating light restrict the maximum number of soliton pairs within the cavity - even down to a single soliton pair - and force long range polarization conformity in trains of vector solitons.

Abstract: Optics is an exciting route for the next generation of computing hardware for machine learning, promising several orders of magnitude enhancement in both computational speed and energy efficiency. However, to reach the full capacity of an optical neural network it is necessary that the computing not only for the inference, but also for the training be implemented optically. The primary algorithm for training a neural network is backpropagation, in which the calculation is performed in the order opposite to the information flow for inference. While straightforward in a digital computer, optical implementation of backpropagation has so far remained elusive, particularly because of the conflicting requirements for the optical element that implements the nonlinear activation function. In this work, we address this challenge for the first time with a surprisingly simple and generic scheme. Saturable absorbers are employed for the role of the activation units, and the required properties are achieved through a pump-probe process, in which the forward propagating signal acts as the pump and backward as the probe. Our approach is adaptable to various analog platforms, materials, and network structures, and it demonstrates the possibility of constructing neural networks entirely reliant on analog optical processes for both training and inference tasks.