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Register a new userTunable Integrated-Optics Nanoscaled Devices Based on Magnetic Photonic Crystals
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Added by
Francoise Heres-Renzetti
Publication date
2007
fields
Physics
and research's language is
English
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Magnetooptical properties of magnetic photonic crystals have been investigated in the view of their possible applications for the modern integrated-optics devices. A transfer matrices formalism was expanded for the case of oblique light incidence on the periodic nanoscaled magnetic multilayered systems. Several new effects such as the Faraday effect dependence on the incidence angle and the tunability of the bandgap defect modes spectral location by external magnetic fields were found. Several possibilities of one-dimensional magnetic photonic crystals applications for the optical devices are discussed. Initial steps towards the practical implementation of the proposed devices are reported.
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The complete symmetry characterization of eigenstates in bare opal systems is obtained by means of group theory. This symmetry assignment has allowed us to identify several bands that cannot couple with an incident external plane wave. Our prediction is supported by layer-KKR calculations, which are also performed: the coupling coefficients between bulk modes and externally excited field tend to zero when symmetry properties mismatch.
In this work, we report our study on the THz emission in Fe/Pt magnetic heterostructures. We have carried out a comprehensive investigation of THz emission from Fe/Pt magnetic heterostructures, employing time-domain THz spectroscopy. We reveal that by properly tuning the thickness of Fe or Pt layer, THz emission can be greatly improved in this type of heterostructure. We further demonstrate that the THz field strength emitted from a newly designed multilayer (Pt/Fe/MgO)$_n$ with n=3 can reach a value of ~1.6 kV/cm, which is comparable to the values from the conventional GaAs antenna with a bias of 4 kV/cm, and the nonlinear crystals, e.g., 100 micrometer GaP and 2 mm ZnTe. For the first time, the intensity and spectrum of THz wave is demonstrated to be tunable by the magnetic field applied on the patterned magnetic Fe/Pt heterostructures. These findings thus promise novel approaches to fabricate powerful and tunable THz emitters based on magnetic heterostructure.
The integration of complex oxides on silicon presents opportunities to extend and enhance silicon technology with novel electronic, magnetic, and photonic properties. Among these materials, barium titanate (BaTiO3) is a particularly strong ferroelectric perovskite oxide with attractive dielectric and electro-optic properties. Here we demonstrate nanophotonic circuits incorporating ferroelectric BaTiO3 thin films on the ubiquitous silicon-on-insulator (SOI) platform. We grow epitaxial, single-crystalline BaTiO3 directly on SOI and engineer integrated waveguide structures that simultaneously confine light and an RF electric field in the BaTiO3 layer. Using on-chip photonic interferometers, we extract a large effective Pockels coefficient of 213 plus minus 49 pm/V, a value more than six times larger than found in commercial optical modulators based on lithium niobate. The monolithically integrated BaTiO3 optical modulators show modulation bandwidth in the gigahertz regime, which is promising for broadband applications.
With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that have been highly successful in applications such as image classification and speech processing. We present an architecture to implement a photonic CNN using the Fourier transform property of integrated star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset. We also model component imperfections in photonic CNN and show that the performance degradation can be recovered in a programmable chip. Our proposed architecture provides a large reduction in physical footprint compared to current implementations as it utilizes the natural advantages of optics and hence offers a scalable pathway towards integrated photonic deep learning processors.
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