Two-photon absorption and self-phase modulation in InGaAsP/InP multi-quantum-well waveguides

We report the first measurement of two-photon absorption (TPA) and self-phase modulation in an InGaAsP/InP multi-quantum-well waveguide. The TPA coefficient, β2, was found to be 60±10 cm/GW at 1.55 μm. Despite operating at 200 nm from the band edge, self-phase modulation as high as 8±2 rad was observed for 30-ps optical pulses at 3.8-W peak input power. A theoretical calculation indicates that this enhanced phase modulation is primarily due to bandfilling in the quantum wells and the free-carrier plasma effect.

First demonstration of two photon absorption in a semiconductor waveguide pumped by a diode laser

For the first time, lasers have been used to induce a fast all-optical nonresonant nonlinearity at wavelengths well beyond the band edge in a GaAs/GaAlAs multiquantum well waveguide. Using a Q-switched diode laser, which gave optical pulses of 3.5 ps duration and 7 W peak power, an intensity-dependent transmission was recorded that was consistent with the presence of two photon absorption in the waveguide. The measured two photon absorption coefficient was 11 ± 2cm/GW.

Two-photon absorption in an atomic cladding wave guide

We experimentally demonstrate two-photon Doppler free interactions on a chip-scale platform consisting of a silicon nitride waveguide integrated with rubidium vapor cladding. We obtain absorption lines having widths of 300 MHz, using low power levels. © OSA 2013.

Two-photon absorption in an atomic cladding wave guide

We experimentally demonstrate two-photon Doppler free interactions on a chip-scale platform consisting of a silicon nitride waveguide integrated with rubidium vapor cladding. We obtain absorption lines having widths of 300 MHz, using low power levels. © OSA 2013.

Measurement of polarisation dependent all-optical nonlinearity in GaAs/AlGaAs quantum well waveguides far from the bandedge

The all-optical nonlinearity of a quantum well waveguide is studied by measuring the intensity dependent transmission through a Fabry-Perot cavity formed around the guide. Values for the nonlinear refractive index coefficient, η 2, at a wavelength of 1.06μm are obtained for light whose polarisation is either parallel or perpendicular to the quantum well layers. A simple measurement to estimate the two photon absorption coefficient, B2, using relatively low optical power levels is also described.

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.

Nonparametric Bayesian Density Modeling with Gaussian Processes

We present the Gaussian process density sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a distribution defined by a density that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We describe two such MCMC methods. Both methods also allow inference of the hyperparameters of the Gaussian process.

Single-photon detection using a quantum dot field effect transistor

A novel device for detection of single photons based on a GaAs/AlGaAs modulation doped field effect transistor (MODFET) which does not rely on avalanche processes is proposed. The optimal channel electron densities and quantum dot parameters for detection of single photons are discussed.

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.

The use of spatial impulse responses to characterise flexible forming processes with mobile tools

A novel test method for the characterisation of flexible forming processes is proposed and applied to four flexible forming processes: Incremental Sheet Forming (ISF), conventional spinning, the English wheel and power hammer. The proposed method is developed in analogy with time-domain control engineering, where a system is characterised by its impulse response. The spatial impulse response is used to characterise the change in workpiece deformation created by a process, but has also been applied with a strain spectrogram, as a novel way to characterise a process and the physical effect it has on the workpiece. Physical and numerical trials to study the effects of process and material parameters on spatial impulse response lead to three main conclusions. Incremental sheet forming is particularly sensitive to process parameters. The English wheel and power hammer are strongly similar and largely insensitive to both process and material parameters. Spinning develops in two stages and is sensitive to most process parameters, but insensitive to prior deformation. Finally, the proposed method could be applied to modelling, classification of existing and novel processes, product-process matching and closed-loop control of flexible forming processes. © 2012 Elsevier B.V.