9,780 research outputs found
A design tool for high-resolution high-frequency cascade continuous- time Σ∆ modulators
Event: Microtechnologies for the New Millennium, 2007, Maspalomas, Gran
Canaria, SpainThis paper introduces a CAD methodology to assist the de
signer in the implementation of continuous-time (CT) cas-
cade
Σ∆
modulators. The salient features of this methodology ar
e: (a) flexible behavioral modeling for optimum accuracy-
efficiency trade-offs at different stages of the top-down
synthesis process; (b) direct synthesis in the continuous-time
domain for minimum circuit complexity and sensitivity; a
nd (c) mixed knowledge-based and optimization-based architec-
tural exploration and specification transmission for enhanced
circuit performance. The applicability of this methodology
will be illustrated via the design of a 12 bit 20 MHz CT
Σ∆
modulator in a 1.2V 130nm CMOS technology.Ministerio de Ciencia y Educación TEC2004-01752/MICMinisterio de Industria, Turismo y Comercio FIT-330100-2006-134 SPIRIT Projec
Quantum Modelling of Electro-Optic Modulators
Many components that are employed in quantum information and communication
systems are well known photonic devices encountered in standard optical fiber
communication systems, such as optical beamsplitters, waveguide couplers and
junctions, electro-optic modulators and optical fiber links. The use of these
photonic devices is becoming increasingly important especially in the context
of their possible integration either in a specifically designed system or in an
already deployed end-to-end fiber link. Whereas the behavior of these devices
is well known under the classical regime, in some cases their operation under
quantum conditions is less well understood. This paper reviews the salient
features of the quantum scattering theory describing both the operation of the
electro-optic phase and amplitude modulators in discrete and continuous-mode
formalisms. This subject is timely and of importance in light of the increasing
utilization of these devices in a variety of systems, including quantum key
distribution and single-photon wavepacket measurement and conformation. In
addition, the paper includes a tutorial development of the use of these models
in selected but yet important applications, such as single and multi-tone
modulation of photons, two-photon interference with phase-modulated light or
the description of amplitude modulation as a quantum operation.Comment: 29 pages, 10 figures, Laser and Photonics Reviews (in press
Output Filter Aware Optimization of the Noise Shaping Properties of {\Delta}{\Sigma} Modulators via Semi-Definite Programming
The Noise Transfer Function (NTF) of {\Delta}{\Sigma} modulators is typically
designed after the features of the input signal. We suggest that in many
applications, and notably those involving D/D and D/A conversion or actuation,
the NTF should instead be shaped after the properties of the
output/reconstruction filter. To this aim, we propose a framework for optimal
design based on the Kalman-Yakubovich-Popov (KYP) lemma and semi-definite
programming. Some examples illustrate how in practical cases the proposed
strategy can outperform more standard approaches.Comment: 14 pages, 18 figures, journal. Code accompanying the paper is
available at http://pydsm.googlecode.co
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
Toward a dynamical systems analysis of neuromodulation
This work presents some first steps toward a
more thorough understanding of the control systems
employed in evolutionary robotics. In order
to choose an appropriate architecture or to construct
an effective novel control system we need
insights into what makes control systems successful,
robust, evolvable, etc. Here we present analysis
intended to shed light on this type of question
as it applies to a novel class of artificial neural
networks that include a neuromodulatory mechanism:
GasNets.
We begin by instantiating a particular GasNet
subcircuit responsible for tuneable pattern generation
and thought to underpin the attractive
property of “temporal adaptivity”. Rather than
work within the GasNet formalism, we develop an
extension of the well-known FitzHugh-Nagumo
equations. The continuous nature of our model
allows us to conduct a thorough dynamical systems
analysis and to draw parallels between this
subcircuit and beating/bursting phenomena reported
in the neuroscience literature.
We then proceed to explore the effects of different
types of parameter modulation on the system
dynamics. We conclude that while there are
key differences between the gain modulation used
in the GasNet and alternative schemes (including
threshold modulation of more traditional synaptic
input), both approaches are able to produce
tuneable pattern generation. While it appears, at
least in this study, that the GasNet’s gain modulation
may not be crucial to pattern generation ,
we go on to suggest some possible advantages it
could confer
Prediction of the Spectrum of a Digital Delta–Sigma Modulator Followed by a Polynomial Nonlinearity
This paper presents a mathematical analysis of the power spectral density of the output of a nonlinear block driven by a digital delta-sigma modulator. The nonlinearity is a memoryless third-order polynomial with real coefficients. The analysis yields expressions that predict the noise floor caused by the nonlinearity when the input is constant
Large-Alphabet Time-Frequency Entangled Quantum Key Distribution by means of Time-to-Frequency Conversion
We introduce a novel time-frequency quantum key distribution (TFQKD) scheme
based on photon pairs entangled in these two conjugate degrees of freedom. The
scheme uses spectral detection and phase modulation to enable measurements in
the temporal basis by means of time-to-frequency conversion. This allows
large-alphabet encoding to be implemented with realistic components. A general
security analysis for TFQKD with binned measurements reveals a close connection
with finite-dimensional QKD protocols and enables analysis of the effects of
dark counts on the secure key size.Comment: 14 pages, 3 figures, submitte
Reference-less measurement of the transmission matrix of a highly scattering material using a DMD and phase retrieval techniques
This paper investigates experimental means of measuring the transmission
matrix (TM) of a highly scattering medium, with the simplest optical setup.
Spatial light modulation is performed by a digital micromirror device (DMD),
allowing high rates and high pixel counts but only binary amplitude modulation.
We used intensity measurement only, thus avoiding the need for a reference
beam. Therefore, the phase of the TM has to be estimated through signal
processing techniques of phase retrieval. Here, we compare four different phase
retrieval principles on noisy experimental data. We validate our estimations of
the TM on three criteria : quality of prediction, distribution of singular
values, and quality of focusing. Results indicate that Bayesian phase retrieval
algorithms with variational approaches provide a good tradeoff between the
computational complexity and the precision of the estimates
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