arXiv subjects

Rajesh Menon

Publications and source records attributed to Rajesh Menon.

At least 37 records · Page 2Linked to original sources

arXiv2022

Ultra-compact synthesis of space-time wave packets

Space-time wave packets (STWPs) are pulsed fields in which a strictly prescribed association between the spatial and temporal frequencies yields surprising and useful behavior. However, STWPs to date have been synthesized using bulky free-space optical systems that require precise alignment. We describe a compact system that makes use of a novel optical component: a chirped volume Bragg grating that is rotated by 45 degrees with respect to the plane-parallel device facets. By virtue of this grating's unique structure, cascaded gratings resolve and recombine the spectrum without free-space propagation or collimation. We produce STWPs by placing a phase plate that spatially modulates the resolved spectrum between such cascaded gratings, with a device volume of 25x25x8 mm3, which is orders of magnitude smaller than previous arrangements.

physics.optics↗

arXiv2022

Space-time wave packets propagating a kilometer in air

We report on the diffraction-free propagation of space-time wave packets (STWPs) -- a class of propagation-invariant pulsed beams -- for $\sim\!1$ km in an open-air laser range in a low-turbulence scenario. Making use of $\approx\!100$-fs pulses (bandwidth $\sim\!25$ nm) at a wavelength of $\approx\!1$ $μ$m, we construct an STWP with a transverse width of $\approx\!2$ mm that expands to $\approx\!3$ mm after $\sim\!500$ m, and another that expands from $\approx\!8$ mm to $\approx\!10$ mm after 1 km. The propagation of the STWPs is compared to Gaussian wave packets of the same transverse spatial width and bandwidth. We establish a theoretical model that accounts for the significant factors limiting the STWP propagation distance and suggests the path to further extending this distance.

physics.optics↗

arXiv2021

Circumventing size-bandwidth limits in imaging with flat lenses

Recent theoretical work suggested upper bounds on the operating bandwidths of flat lenses. Here, we show how these bounds can be circumvented via a multi-level diffractive lens (MDL) of diameter = 100 mm, focal length = 200 mm, device thickness = 2.4μ m and operating bandwidth from ? = 400 nm to 800 nm. We further combine the MDL with a refractive lens to demonstrate a hybrid telescope. By appealing to coherence theory, we show that the upper bound on relative bandwidth is surprisingly independent of lens diameter or numerical aperture, but is only limited by the bandwidth of the image sensor. Since large-area achromatic flat lenses produce significant reductions in weight over their refractive counterparts, these calculations and experiments open up opportunities for very large scale diffractive and diffractive-refractive telescopes.

physics.optics↗

arXiv2021

Monolithic all-Silicon Flat lens for broadband LWIR Imaging

We designed, fabricated and characterized a flat multi-level diffractive lens (MDL) comprised of only silicon with diameter = 15.2mm, focal length of 19mm, and operating over the longwave infrared (LWIR) spectrum of 8um to 14um. We experimentally demonstrated field of view of 46deg, depth of focus >7mm and wavelength-averaged Strehl ratio of 0.46. All these metrics were comparable to those of a conventional refractive lens. The active device thickness is only 8um and its weight (including the silicon substrate) is less than 0.2g.

physics.optics↗

arXiv2020

Machine Learning enables Ultra-Compact Integrated Photonics through Silicon-Nanopattern Digital Metamaterials

In this work, we demonstrate three ultra-compact integrated-photonics devices, which are designed via a machine-learning algorithm coupled with finite-difference time-domain (FDTD) modeling. Through digitizing the design domain into "binary pixels" these digital metamaterials are readily manufacturable as well. By showing a variety of devices (beamsplitters and waveguide bends), we showcase the generality of our approach. With an area footprint smaller than ${λ_0}^2$, our designs are amongst the smallest reported to-date. Our method combines machine learning with digital metamaterials to enable ultra-compact, manufacturable devices, which could power a new "Photonics Moore's Law."

physics.optics↗

arXiv2020

Machine Learning enables Design of On-chip Integrated Silicon T-junctions with footprint of 1.2 $μ$m x 1.2 $μ$m

To date, various optimization algorithms have been employed to design and improve the performance of nanophotonic structures. Here, we propose to utilize a machine-learning algorithm viz. binary-Additive Reinforcement Learning Algorithm (b-ARLA) coupled with finite-difference time-domain (FDTD) simulations to design ultra-compact and efficient on-chip integrated nanophotonic 50:50 beam splitters (T-junctions). Here we present the design of two T-junction splitters each with a footprint of only 1.2 $μ$m x 1.2 $μ$m. To the best of our knowledge, these designs are amongst the smallest ever reported till date across either simulations or experiments. The simulated net power transmission efficiency for the first T-junction design is ~ 82% and the second design is ~ 80% $at 4λ= 1.55 μ$m. We envision that the design methodology, as reported herein, would be useful in general for designing any efficient integrated-photonic device for optical communications systems.

physics.app-ph↗

arXiv2020

A needle-based deep-neural-network camera

We experimentally demonstrate a camera whose primary optic is a cannula (diameter=0.22mm and length=12.5mm) that acts a lightpipe transporting light intensity from an object plane (35cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with field of view of 180 and angular resolution of ~0.40. When trained on images with depth information, the DNN can create depth maps. Finally, we show DNN-based classification of the EMNIST dataset without and with image reconstructions. The former could be useful for imaging with enhanced privacy.

eess.IV↗

arXiv2020

Classification of optics-free images with deep neural networks

The thinnest possible camera is achieved by removing all optics, leaving only the image sensor. We train deep neural networks to perform multi-class detection and binary classification (with accuracy of 92%) on optics-free images without the need for anthropocentric image reconstructions. Inferencing from optics-free images has the potential for enhanced privacy and power efficiency.

eess.IV↗

arXiv2020

Super-resolution imaging with an achromatic multi-level diffractive microlens array

Compound eyes found in insects provide intriguing sources of biological inspiration for miniaturized imaging systems. Inspired by such insect eye structures, we demonstrate an ultrathin arrayed camera enabled by a flat multilevel diffractive microlens array for super-resolution visible imaging. We experimentally demonstrated that the microlens array can achieve large fill factor (hexagonal close packing with pitch=120um), thickness of 2.6um, and diffraction-limited (strehl ratio = 0.88) achromatic performance in the visible band (450nm to 650nm). We also demonstrate super-resolution imaging with resolution improvement of 1.4 times by computationally merging 1600 images in the array.

physics.optics↗

arXiv2020

Free-form broadband flat lenses for visible imaging

In the absence of separate apertures, the size and focal length of a lens determines both its resolution and light-collection ability defined by numerical-aperture (NA) and f/#, respectively. We utilize free-form broadband-diffractive-optical elements (BDOEs) to create flat lenses that decouple NA from f#, whereby the resolution of the lens may be chosen independently from its light-collection ability. Specifically, we designed, fabricated and characterized three BDOE lenses operating in the visible band (450nm-750nm), each with f/11.25, but with NAs of 0.00075, 0.0067 and 0.054, respectively. Simulations confirm that such decoupling is possible even at much higher NAs. Experiments confirm achromatic focusing and broadband imaging. One of the lenses exhibited a depth-of-focus almost 2 orders of magnitude larger than the diffraction limit. Such BDOE lenses could be very useful in focal-plane arrays with large pixel sizes, where light collection efficiency needs to be maintained. Furthermore, by abandoning rotational symmetry, one can achieve free-form geometries in the focal spot, such as a square that can more closely match the geometry of the sensor pixel.

physics.optics↗

arXiv2020

3D Computational Cannula Fluorescence Microscopy enabled by Artificial Neural Networks

Computational Cannula Microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and fluorescence emission, where computational methods are used for image visualization. Here, we enhance CCM with artificial neural networks to enable 3D imaging of cultured neurons and fluorescent beads, the latter inside a volumetric phantom. We experimentally demonstrate transverse resolution of ~6um, field of view ~200um and axial sectioning of ~50um for depths down to ~700um, all achieved with computation time of ~3ms/frame on a laptop computer.

eess.IV↗

arXiv2020

Learning Wavefront Coding for Extended Depth of Field Imaging

Depth of field is an important factor of imaging systems that highly affects the quality of the acquired spatial information. Extended depth of field (EDoF) imaging is a challenging ill-posed problem and has been extensively addressed in the literature. We propose a computational imaging approach for EDoF, where we employ wavefront coding via a diffractive optical element (DOE) and we achieve deblurring through a convolutional neural network. Thanks to the end-to-end differentiable modeling of optical image formation and computational post-processing, we jointly optimize the optical design, i.e., DOE, and the deblurring through standard gradient descent methods. Based on the properties of the underlying refractive lens and the desired EDoF range, we provide an analytical expression for the search space of the DOE, which is instrumental in the convergence of the end-to-end network. We achieve superior EDoF imaging performance compared to the state of the art, where we demonstrate results with minimal artifacts in various scenarios, including deep 3D scenes and broadband imaging.

eess.IV↗

arXiv2020

Inverse-designed flat lens for imaging in the visible & near-infrared with diameter > 3mm and NA=0.3

It is generally thought that correcting chromatic aberrations in imaging requires multiple surfaces. Here, we show that by allowing the phase in the image plane of a flat lens to be a free parameter, it is possible to correct chromatic aberrations over a large continuous bandwidth with a single diffractive surface. We experimentally demonstrate imaging using a single flat lens of diameter > 3mm, focal length = 5mm (NA = 0.3, f/1.59) that is achromatic from 0.45um to 1um. This combination of size, NA and operating bandwidth has not been demonstrated in a flat lens before. We experimentally characterized the point-spread functions, off-axis aberrations and the broadband imaging performance. In contrast to conventional lens design, we utilize inverse design, where phase in the focal plane is treated as a free parameter. This approach attains a phase-only (lossless) pupil function, which can be implemented as a multi-level diffractive flat lens that achieves achromatic focusing and imaging.

physics.optics↗

arXiv2020

Optics-free imaging of complex, non-sparse QR-codes with Deep Neural Networks

We demonstrate optics-free imaging of complex QR-codes using a bare image sensor and a trained artificial neural network (ANN). The ANN is trained to interpret the raw sensor data for human visualization. The image sensor is placed at a specified gap from the QR code. We studied the robustness of our approach by experimentally testing the output of the ANNs with system perturbations of this gap, and the translational and rotational alignments of the QR code to the image sensor. Our demonstration opens us the possibility of using completely optics-free cameras for application-specific imaging of complex, non-sparse objects.

eess.IV↗

arXiv2020

Large-area, high-NA Multi-level Diffractive Lens via inverse design

Flat lenses enable thinner, lighter, and simpler imaging systems. However, large-area and high-NA flat lenses have been elusive due to computational and fabrication challenges. Here, we applied inverse design to create a multi-level diffractive lens (MDL) with thickness <1.35μm, diameter of 4.13mm, NA=0.9 at wavelength of 850nm. Since the MDL is created in polymer, it can be cost-effectively replicated via imprint lithography.

physics.optics↗

arXiv2020

Imaging from the Visible to the Longwave Infrared wavelengths via an inverse-designed flat lens

It is generally assumed that correcting chromatic aberrations in imaging requires optical elements. Here, we show that by allowing the phase in the image plane to be a free parameter, it is possible to correct chromatic variation of focal length over an extremely large bandwidth, from the visible (Vis) to the longwave infrared (LWIR) wavelengths using a single diffractive surface, i.e., a flat lens. Specifically, we designed, fabricated and characterized a flat, multi-level diffractive lens (MDL) with thickness £ 10μm, diameter ~1mm, and focal length = 18mm, which was constant over the operating bandwidth of l=0.45μm (blue) to 15μm (LWIR). We experimentally characterized the point-spread functions, aberrations and imaging performance of cameras comprised of this MDL and appropriate image sensors. We further show using simulations that such extreme achromatic MDLs can be achieved even at high numerical apertures (NA=0.81). By drastically increasing the operating bandwidth and eliminating several refractive lenses, our approach enables thinner, lighter and simpler imaging systems.

physics.optics↗

arXiv2020

Computational Cannula Microscopy of neurons using Neural Networks

Computational Cannula Microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable fast, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with field of view of 200$μ$m (diameter) and resolution of less than 10$μ$m using a cannula of diameter of only 220$μ$m. In addition, we show that this approach can also be extended to macro-photography.

eess.IV↗

arXiv2019

Multi-plane, Multi-band image projection via Broadband Diffractive Optics

We demonstrate visible and near-IR image projection via non-absorbing, multi-level Broadband Diffractive-Optical Elements (BDOEs) in 1 or more planes. By appropriate design of the BDOE topography, we experimentally demonstrate: (1) different images in different spectral bands; (2) different images in different image planes; (3) image magnification by changing the distance between the illumination source and the BDOE; (4) completely flat BDOE via an index-contrast top-coating; and (5) reflective BDOEs. All of these are accomplished with broadband illumination. Furthermore, the BDOEs are highly efficient, versatile and can be inexpensive mass manufactured using imprint-based replication techniques.

physics.optics↗