Nonlinear optics in Silicon photonic crystal cavities

Silicon is known to be a very good material for the realization of high-Q, low-volume photonic cavities, but at the same it is usually considered as a poor material for nonlinear optical functionalities like second-harmonic generation, because its second-order nonlinear susceptibility vanishes in the dipole approximation. In this work we demonstrate that nonlinear optical effects in silicon nanocavities can be strongly enhanced and even become macroscopically observable. We employ photonic crystal nanocavities in silicon membranes that are optimized simultaneously for high quality factor and efficient coupling to an incoming beam in the far field. Using a low-power, continuous-wave laser at telecommunication wavelengths as a pump beam, we demonstrate simultaneous generation of second- and third harmonics in the visible region, which can be observed with a simple camera. The results are in good agreement with a theoretical model that treats third-harmonic generation as a bulk effect in the cavity region, and second-harmonic generation as a surface effect arising from the vertical hole sidewalls. Optical bistability is also observed in the silicon nanocavities and its physical mechanisms (optical, due to two-photon generation of free carriers, as well as thermal) are investigated. © 2011 IEEE.

Beamforming correction for dipole measurement using two-dimensional microphone arrays

In this paper, a beamforming correction for identifying dipole sources by means of phased microphone array measurements is presented and implemented numerically and experimentally. Conventional beamforming techniques, which are developed for monopole sources, can lead to significant errors when applied to reconstruct dipole sources. A previous correction technique to microphone signals is extended to account for both source location and source power for two-dimensional microphone arrays. The new dipole-beamforming algorithm is developed by modifying the basic source definition used for beamforming. This technique improves the previous signal correction method and yields a beamformer applicable to sources which are suspected to be dipole in nature. Numerical simulations are performed, which validate the capability of this beamformer to recover ideal dipole sources. The beamforming correction is applied to the identification of realistic aeolian-tone dipoles and shows an improvement of array performance on estimating dipole source powers. © 2008 Acoustical Society of America.

Low-temperature (≤600 °C) semi-insulating oxygen-doped silicon films by the PECVD technique for large-area power applications

This work describes the deposition, annealing and characterisation of semi-insulating oxygen-doped silicon films at temperatures compatible with polysilicon circuitry on glass. The semi-insulating layers are deposited by the plasma enhanced chemical vapour deposition technique from silane (SiH4), nitrous oxide (N2O) and helium (He) gas mixtures at a temperature of 350 °C. The as-deposited films are then furnace annealed at 600 °C which is the maximum process temperature. Raman analysis shows the as-deposited and annealed films to be completely amorphous. The most important deposition variable is the N2O SiH4 gas ratio. By varying the N2O SiH4 ratio the conductivity of the annealed films can be accurately controlled, for the first time, down to a minimum of ≈10-7Ω-1cm-1 where they exhibit a T -1 4 temperature dependence indicative of a hopping conduction mechanism. Helium dilution of the reactant gases is shown to improve both film uniformity and reproducibility. A model for the microstructure of these semi-insulating amorphous oxygen-doped silicon films is proposed to explain the observed physical and electrical properties. © 1995.

Low temperature (≤ 600°C) semi-insulating oxygen-doped silicon films by the PECVD technique for large area power applications

This work describes the annealing and characterisation of semi-insulating oxygen-doped silicon films deposited by the Plasma Enhanced Chemical Vapour Deposition (PECVD) technique from silane (SiH4), nitrous oxide (N2O) and helium (He) gas mixtures. The maximum process temperature is chosen to be compatible with large area polycrystalline silicon (poly-Si) circuitry on glass. The most important deposition variable is shown to be the N2O SiH4 gas ratio. Helium dilution results in improved film uniformity and reproducibility. Raman analysis shows the 'as-deposited' and annealed films to be completely amorphous. A model for the microstructure of these Semi-Insulating Amorphous Oxygen-doped Silicon (SIAOS) films is proposed to explain the observed physical and electrical properties. © 1995.

Probing biomolecular interaction forces using an anharmonic acoustic technique for selective detection of bacterial spores.

Receptor-based detection of pathogens often suffers from non-specific interactions, and as most detection techniques cannot distinguish between affinities of interactions, false positive responses remain a plaguing reality. Here, we report an anharmonic acoustic based method of detection that addresses the inherent weakness of current ligand dependant assays. Spores of Bacillus subtilis (Bacillus anthracis simulant) were immobilized on a thickness-shear mode AT-cut quartz crystal functionalized with anti-spore antibody and the sensor was driven by a pure sinusoidal oscillation at increasing amplitude. Biomolecular interaction forces between the coupled spores and the accelerating surface caused a nonlinear modulation of the acoustic response of the crystal. In particular, the deviation in the third harmonic of the transduced electrical response versus oscillation amplitude of the sensor (signal) was found to be significant. Signals from the specifically-bound spores were clearly distinguishable in shape from those of the physisorbed streptavidin-coated polystyrene microbeads. The analytical model presented here enables estimation of the biomolecular interaction forces from the measured response. Thus, probing biomolecular interaction forces using the described technique can quantitatively detect pathogens and distinguish specific from non-specific interactions, with potential applicability to rapid point-of-care detection. This also serves as a potential tool for rapid force-spectroscopy, affinity-based biomolecular screening and mapping of molecular interaction networks.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.

Filtering via approximate Bayesian computation

Approximate Bayesian computation (ABC) has become a popular technique to facilitate Bayesian inference from complex models. In this article we present an ABC approximation designed to perform biased filtering for a Hidden Markov Model when the likelihood function is intractable. We use a sequential Monte Carlo (SMC) algorithm to both fit and sample from our ABC approximation of the target probability density. This approach is shown to, empirically, be more accurate w.r.t.~the original filter than competing methods. The theoretical bias of our method is investigated; it is shown that the bias goes to zero at the expense of increased computational effort. Our approach is illustrated on a constrained sequential lasso for portfolio allocation to 15 constituents of the FTSE 100 share index.