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Photon statistics and dynamics of nanolasers subject to intensity feedback
摘要： Using a fully stochastic numerical scheme, we theoretically investigate the behavior of a nanolaser in the lasing transition region under the influence of intensity feedback. Studying the input-output curves as well as the second-order correlations for different feedback fractions, we obtain an insight into the role played by the fraction of photons reinjected into the cavity. Our results reveal that the transition shrinks and moves to lower pump values with the feedback strength, and irregular pulses can be generated when the feedback is sufficiently large. The interpretation of the observation is strengthened through the comparison with the temporal traces of the emitted photons and with the radio-frequency power spectra. These results provide insight into the physics of nanolasers as well as validating the use of the second-order autocorrelation as a sufficient tool for interpretation of the dynamics. This confirmation offers a solid basis for the reliance on autocorrelations in experiments studying the effects of feedback in nanodevices.
关键词： nanolasers,stochastic simulation,intensity feedback,photon statistics,autocorrelation
更新于2025-09-23 15:19:57
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The time response of plasmonic sensors due to binary adsorption: analytical versus numerical modeling
摘要： In order to allow for multiscale modeling of complex systems, we focus on various approaches to modeling binary adsorption. We consider multiple methods of modeling the temporal response of general plasmonic sensors. We start from the analytical approach. The kinetics of adsorption and desorption is modeled both as a first order reaction and as a second-order reaction. The criteria for their validity and the choice between them in the case of two-component adsorption are established. Due to the nonlinearities of the second-order reactions and the lack of their analytical solutions, computer-aided modeling is considered next, also in multiple ways: the employment of numerical solvers, fitting of experimental results, the stochastic simulation algorithms and the employment of artificial neural networks (ANN). The examples we present illustrate the advantages and disadvantages of the particular approaches. The goal is to aid the concurrent multiscale modeling of adsorption-based devices. Machine learning in ANN performed here is used to estimate the equilibrium values of adsorbed quantities. The obtained results show that to train an ANN for the estimation of the equilibrium adsorption quantities the Levenberg–Marquardt and the Bayesian regularization algorithms are less efficient than the quasi-Newton BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm.
关键词： Kinetics,Stochastic simulation algorithms,Adsorption,Machine learning algorithms,Plasmonic sensing
更新于2025-09-23 15:19:57