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.

Nanoscale light-matter interactions in atomic cladding waveguides.

Alkali vapours, such as rubidium, are being used extensively in several important fields of research such as slow and stored light nonlinear optics quantum computation, atomic clocks and magnetometers. Recently, there is a growing effort towards miniaturizing traditional centimetre-size vapour cells. Owing to the significant reduction in device dimensions, light-matter interactions are greatly enhanced, enabling new functionalities due to the low power threshold needed for nonlinear interactions. Here, taking advantage of the mature platform of silicon photonics, we construct an efficient and flexible platform for tailored light-vapour interactions on a chip. Specifically, we demonstrate light-matter interactions in an atomic cladding waveguide, consisting of a silicon nitride nano-waveguide core with a rubidium vapour cladding. We observe the efficient interaction of the electromagnetic guided mode with the rubidium cladding and show that due to the high confinement of the optical mode, the rubidium absorption saturates at powers in the nanowatt regime.

Plasmonic nanofocusing of light in an integrated silicon photonics platform.

The capability to focus electromagnetic energy at the nanoscale plays an important role in nanoscinece and nanotechnology. It allows enhancing light matter interactions at the nanoscale with applications related to nonlinear optics, light emission and light detection. It may also be used for enhancing resolution in microscopy, lithography and optical storage systems. Hereby we propose and experimentally demonstrate the nanoscale focusing of surface plasmons by constructing an integrated plasmonic/photonic on chip nanofocusing device in silicon platform. The device was tested directly by measuring the optical intensity along it using a near-field microscope. We found an order of magnitude enhancement of the intensity at the tip's apex. The spot size is estimated to be 50 nm. The demonstrated device may be used as a building block for "lab on a chip" systems and for enhancing light matter interactions at the apex of the tip.

Nonlinear pulse switching and compression in a dispersion-imbalanced loop mirror

The results of the high-quality nonlinear pulse compression of gain-switched laser diode pulses using a two-cascade compression scheme are presented. The scheme incorporates a dispersive delay line and a nonlinear pulse compressor based on a dispersion-imbalanced fiber loop mirror (DILM). It is demonstrated that the DILM can be also used for the pulse compression with a compression ratio of 10 or higher.

Modelling nonlinear vehicle dynamics with neural networks

The nonlinear modelling ability of neural networks has been widely recognised as an effective tool to identify and control dynamic systems, with applications including nonlinear vehicle dynamics which this paper focuses on using multi-layer perceptron networks. Existing neural network literature does not detail some of the factors which effect neural network nonlinear modelling ability. This paper investigates into and concludes on required network size, structure and initial weights, considering results for networks of converged weights. The paper also presents an online training method and an error measure representing the network's parallel modelling ability over a range of operating conditions. Copyright © 2010 Inderscience Enterprises Ltd.