149 research outputs found
Still an Opaque Institution? Explaining Decision-Making in the EU Council Using Newspaper Information: A Reply to Sullivan and Veen
Cataloged from PDF version of article
Ultrafast carrier dynamics in terahertz photoconductors and photomixers: beyond short-carrier-lifetime semiconductors
Efficient terahertz generation and detection are a key prerequisite for high performance terahertz systems. Major advancements in realizing efficient terahertz emitters and detectors were enabled through photonics-driven semiconductor devices, thanks to the extremely wide bandwidth available at optical frequencies. Through the efficient generation and ultrafast transport of charge carriers within a photo-absorbing semiconductor material, terahertz frequency components are created from the mixing products of the optical frequency components that drive the terahertz device – a process usually referred to as photomixing. The created terahertz frequency components, which are in the physical form of oscillating carrier concentrations, can feed a terahertz antenna and get radiated in case of a terahertz emitter, or mix with an incoming terahertz wave to down-convert to DC or to a low frequency photocurrent in case of a terahertz detector. Realizing terahertz photoconductors typically relies on short-carrier-lifetime semiconductors as the photo-absorbing material, where photocarriers are quickly trapped within one picosecond or less after generation, leading to ultrafast carrier dynamics that facilitates high-frequency device operation. However, while enabling broadband operation, a sub-picosecond lifetime of the photocarriers results in a substantial loss of photoconductive gain and optical responsivity. In addition, growth of short-carrier-lifetime semiconductors in many cases relies on the use of rare elements and non-standard processes with limited accessibility. Therefore, there is a strong motivation to explore and develop alternative techniques for realizing terahertz photomixers that do not rely on these defect-introduced short-carrier-lifetime semiconductors. This review will provide an overview of several promising approaches to realize terahertz emitters and detectors without short-carrier-lifetime semiconductors. These novel approaches utilize p-i-n diode junctions, plasmonic nanostructures, ultrafast spintronics, and low-dimensional materials to offer ultrafast carrier response. These innovative directions have great potentials for extending the applicability and accessibility of the terahertz spectrum for a wide range of applications
Terahertz Pulse Shaping Using Diffractive Surfaces
Recent advances in deep learning have been providing non-intuitive solutions
to various inverse problems in optics. At the intersection of machine learning
and optics, diffractive networks merge wave-optics with deep learning to design
task-specific elements to all-optically perform various tasks such as object
classification and machine vision. Here, we present a diffractive network,
which is used to shape an arbitrary broadband pulse into a desired optical
waveform, forming a compact pulse engineering system. We experimentally
demonstrate the synthesis of square pulses with different temporal-widths by
manufacturing passive diffractive layers that collectively control both the
spectral amplitude and the phase of an input terahertz pulse. Our results
constitute the first demonstration of direct pulse shaping in terahertz
spectrum, where a complex-valued spectral modulation function directly acts on
terahertz frequencies. Furthermore, a Lego-like physical transfer learning
approach is presented to illustrate pulse-width tunability by replacing part of
an existing network with newly trained diffractive layers, demonstrating its
modularity. This learning-based diffractive pulse engineering framework can
find broad applications in e.g., communications, ultra-fast imaging and
spectroscopy.Comment: 27 pages, 6 figure
Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
3D engineering of matter has opened up new avenues for designing systems that
can perform various computational tasks through light-matter interaction. Here,
we demonstrate the design of optical networks in the form of multiple
diffractive layers that are trained using deep learning to transform and encode
the spatial information of objects into the power spectrum of the diffracted
light, which are used to perform optical classification of objects with a
single-pixel spectroscopic detector. Using a time-domain spectroscopy setup
with a plasmonic nanoantenna-based detector, we experimentally validated this
machine vision framework at terahertz spectrum to optically classify the images
of handwritten digits by detecting the spectral power of the diffracted light
at ten distinct wavelengths, each representing one class/digit. We also report
the coupling of this spectral encoding achieved through a diffractive optical
network with a shallow electronic neural network, separately trained to
reconstruct the images of handwritten digits based on solely the spectral
information encoded in these ten distinct wavelengths within the diffracted
light. These reconstructed images demonstrate task-specific image decompression
and can also be cycled back as new inputs to the same diffractive network to
improve its optical object classification. This unique machine vision framework
merges the power of deep learning with the spatial and spectral processing
capabilities of diffractive networks, and can also be extended to other
spectral-domain measurement systems to enable new 3D imaging and sensing
modalities integrated with spectrally encoded classification tasks performed
through diffractive optical networks.Comment: 21 pages, 5 figures, 1 tabl
Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor
Terahertz waves offer numerous advantages for the nondestructive detection of
hidden objects/defects in materials, as they can penetrate through most
optically-opaque materials. However, existing terahertz inspection systems are
restricted in their throughput and accuracy (especially for detecting small
features) due to their limited speed and resolution. Furthermore, machine
vision-based continuous sensing systems that use large-pixel-count imaging are
generally bottlenecked due to their digital storage, data transmission and
image processing requirements. Here, we report a diffractive processor that
rapidly detects hidden defects/objects within a target sample using a
single-pixel spectroscopic terahertz detector, without scanning the sample or
forming/processing its image. This terahertz processor consists of passive
diffractive layers that are optimized using deep learning to modify the
spectrum of the terahertz radiation according to the absence/presence of hidden
structures or defects. After its fabrication, the resulting diffractive
processor all-optically probes the structural information of the sample volume
and outputs a spectrum that directly indicates the presence or absence of
hidden structures, not visible from outside. As a proof-of-concept, we trained
a diffractive terahertz processor to sense hidden defects (including
subwavelength features) inside test samples, and evaluated its performance by
analyzing the detection sensitivity as a function of the size and position of
the unknown defects. We validated its feasibility using a single-pixel
terahertz time-domain spectroscopy setup and 3D-printed diffractive layers,
successfully detecting hidden defects using pulsed terahertz illumination. This
technique will be valuable for various applications, e.g., security screening,
biomedical sensing, quality control, anti-counterfeiting measures and cultural
heritage protection.Comment: 23 Pages, 5 Figure
Wavelength conversion through plasmon-coupled surface states
Surface states generally degrade semiconductor device performance by raising
the charge injection barrier height, introducing localized trap states,
inducing surface leakage current, and altering the electric potential.
Therefore, there has been an endless effort to use various surface passivation
treatments to suppress the undesirable impacts of the surface states. We show
that the giant built-in electric field created by the surface states can be
harnessed to enable passive wavelength conversion without utilizing any
nonlinear optical phenomena. Photo-excited surface plasmons are coupled to the
surface states to generate an electron gas, which is routed to a nanoantenna
array through the giant electric field created by the surface states. The
induced current on the nanoantennas, which contains mixing product of different
optical frequency components, generates radiation at the beat frequencies of
the incident photons. We utilize the unprecedented functionalities of
plasmon-coupled surface states to demonstrate passive wavelength conversion of
nanojoule optical pulses at a 1550 nm center wavelength to terahertz regime
with record-high efficiencies that exceed nonlinear optical methods by 4-orders
of magnitude. The presented scheme can be used for optical wavelength
conversion to different parts of the electromagnetic spectrum ranging from
microwave to infrared regimes by using appropriate optical beat frequencies.Comment: Manuscript: 8 pages, 4 figures Supplementary materials: 21 pages, 11
figure
Wheat and hazelnut inspection with impact acoustics time-frequency patterns
Kernel damage caused by insects and fungi is one of the most common reason for poor flour quality. Cracked hazelnut shells are prone to infection by cancer producing mold. We propose a new adaptive time-frequency classification procedure for detecting cracked hazelnut shells and damaged wheat kernels using impact acoustic emissions recorded by dropping wheat kernels or hazelnut shells on a steel plate. The proposed algorithm is based on a flexible local discriminant bases (F-LDB) procedure. The F-LDB method combines local cosine packet analysis and a frequency axis clustering approach which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post processed by principal component analysis and fed to linear discriminant. We describe experimental results that establish the superior performance of the proposed approach when compared with prior techniques reported in the literature or used in the field. Our approach achieved classification accuracy in paired separation of undamaged wheat kernels from IDK, Pupae and Scab damaged kernels with 96%, 82% and 94%. For hazelnuts the accuracy was 97.1%
- …