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Register a new userAnalog Optical Computing by Half-Wavelength Slabs
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Added by
Farzad Zangeneh-Nejad Mr.
Publication date
2017
fields
Physics
and research's language is
English
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A new approach to perform analog optical differentiation is presented using half-wavelength slabs. First, a half-wavelength dielectric slab is used to design a first order differentiator. The latter works properly for both major polarizations, in contrast to designs based on Brewster effect [Opt. Lett. 41, 3467 (2016)]. Inspired by the proposed dielectric differentiator, and by exploiting the unique features of graphene, we further design and demonstrate a reconfigurable and highly miniaturized differentiator using a half-wavelength plasmonic graphene film. To the best of our knowledge, our proposed graphene-based differentiator is even smaller than the most compact differentiator presented so far [Opt. Lett. 40, 5239 (2015)].
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Analog photonic solutions offer unique opportunities to address complex computational tasks with unprecedented performance in terms of energy dissipation and speeds, overcoming current limitations of modern computing architectures based on electron flows and digital approaches. The lack of modularization and lumped element reconfigurability in photonics has prevented the transition to an all-optical analog computing platform. Here, we explore a nanophotonic platform based on epsilon-near-zero materials capable of solving in the analog domain partial differential equations (PDE). Wavelength stretching in zero-index media enables highly nonlocal interactions within the board based on the conduction of electric displacement, which can be monitored to extract the solution of a broad class of PDE problems. By exploiting control of deposition technique through process parameters, we demonstrate the possibility of implementing the proposed nano-optic processor using CMOS-compatible indium-tin-oxide, whose optical properties can be tuned by carrier injection to obtain programmability at high speeds and low energy requirements. Our nano-optical analog processor can be integrated at chip-scale, processing arbitrary inputs at the speed of light.
Optical computing has emerged as a promising candidate for real-time and parallel continuous data processing. Motivated by recent progresses in metamaterial-based analog computing [Science 343, 160 (2014)], we theoretically investigate realization of two-dimensional complex mathematical operations using rotated configurations, recently reported in [Opt. Lett. 39, 1278 (2014)]. Breaking the reflection symmetry, such configurations could realize both even and odd Greens functions associated with spatial operators. Based on such appealing theory and by using Brewster effect, we demonstrate realization of a first-order differentiator. Such efficient wave-based computation method not only circumvents the major potential drawbacks of metamaterials, but also offers the most compact possible device compared to the conventional bulky lens-based optical signal and data processors.
A finite element-based modal formulation of diffraction of a plane wave by an absorbing photonic crystal slab of arbitrary geometry is developed for photovoltaic applications. The semi-analytic approach allows efficient and accurate calculation of the absorption of an array with a complex unit cell. This approach gives direct physical insight into the absorption mechanism in such structures, which can be used to enhance the absorption. The verification and validation of this approach is applied to a silicon nanowire array and the efficiency and accuracy of the method is demonstrated. The method is ideally suited to studying the manner in which spectral properties (e.g., absorption) vary with the thickness of the array, and we demonstrate this with efficient calculations which can identify an optimal geometry.
Despite recent progress in nonlinear optics in wavelength-scale resonators, there are still open questions on the possibility of parametric oscillation in such resonators. We present a general approach to predict the behavior and estimate the oscillation threshold of multi-mode subwavelength and wavelength-scale optical parametric oscillators (OPOs). As an example, we propose an OPO based on Mie-type multipolar resonances, and we demonstrate that due to the low-Q nature of multipolar modes in wavelength-scale resonators, there is a nonlinear interaction between these modes. As a result, the OPO threshold, compared to the single-mode case, can be reduced by a factor that is significantly larger than the number of interacting modes. The multi-mode interaction can also lead to a phase transition manifested through a sudden change in the parametric gain as well as the oscillation threshold, which can be utilized for enhanced sensing. We establish an explicit connection between the second-harmonic generation efficiency and the OPO threshold. This allows us to estimate the OPO threshold based on measured or simulated second-harmonic generation in different classes of resonators, such as bound states in the continuum and inversely designed resonators. Our approach for analyzing and modeling miniaturized OPOs can open unprecedented opportunities for classical and quantum nonlinear photonics.
This paper presents the concepts behind the BrainScales (BSS) accelerated analog neuromorphic computing architecture. It describes the second-generation BrainScales-2 (BSS-2) version and its most recent in-silico realization, the HICANN-X Application Specific Integrated Circuit (ASIC), as it has been developed as part of the neuromorphic computing activities within the European Human Brain Project (HBP). While the first generation is implemented in an 180nm process, the second generation uses 65nm technology. This allows the integration of a digital plasticity processing unit, a highly-parallel micro processor specially built for the computational needs of learning in an accelerated analog neuromorphic systems. The presented architecture is based upon a continuous-time, analog, physical model implementation of neurons and synapses, resembling an analog neuromorphic accelerator attached to build-in digital compute cores. While the analog part emulates the spike-based dynamics of the neural network in continuous-time, the latter simulates biological processes happening on a slower time-scale, like structural and parameter changes. Compared to biological time-scales, the emulation is highly accelerated, i.e. all time-constants are several orders of magnitude smaller than in biology. Programmable ion channel emulation and inter-compartmental conductances allow the modeling of nonlinear dendrites, back-propagating action-potentials as well as NMDA and Calcium plateau potentials. To extend the usability of the analog accelerator, it also supports vector-matrix multiplication. Thereby, BSS-2 supports inference of deep convolutional networks as well as local-learning with complex ensembles of spiking neurons within the same substrate.
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