Project description:Bending light along arbitrary curvatures is a captivating and popular notion, triggering unprecedented endeavors in achieving diffraction-free propagation along a curved path in free-space. Much effort has been devoted to achieving this goal in homogeneous space, which solely relies on the transverse acceleration of beam centroid exerted by a beam generator. Here, based on an all-dielectric metasurface, we experimentally report a synthetic strategy of encoding and multiplexing acceleration features on a freely propagating light beam, synergized with photonic spin states of light. Independent switching between two arbitrary visible accelerating light beams with distinct acceleration directions and caustic trajectories is achieved. This proof-of-concept recipe demonstrates the strength of the designed metasurface chip: subwavelength pixel size, independent control over light beam curvature, broadband operation in the visible, and ultrathin scalable planar architecture. Our results open up the possibility of creating ultracompact, high-pixel density, and flat-profile nanophotonic platforms for efficient generation and dynamical control of structured light beams.
Project description:In numerous applications from radio to optical frequencies including stealth and energy harvesting, there is a need to design electrically thin layers capable of perfectly absorbing electromagnetic waves over a wide bandwidth. However, a theoretical upper bound exists on the bandwidth-to-thickness ratio of metal-backed, passive, linear, and time-invariant absorbing layers. Absorbers developed to date, irrespective of their operational frequency range or material thickness, significantly underperform when compared to this upper bound, failing to exploit the full potential that passive, linear, and time-invariant systems can provide. Here, we introduce a new concept for designing ultra-thin absorbers that enables absorbing layers with a record-high bandwidth-to-thickness ratio, potentially several times greater than that of absorbers designed using conventional approaches. Absorbers designed based on this concept can achieve a bandwidth-to-thickness ratio arbitrarily close to the ultimate bound. Utilizing this concept, we design and experimentally verify an absorber yielding a very high bandwidth-to-thickness ratio.
Project description:Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial attention. However, broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic circuitry. Here, we report an analog parallel processor (APP) based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog domain. The APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2N parallel sequences. Via photonic analog parallelism, data rate and data volume in each sequence are simultaneously compressed, which mitigates the requirement on each parallel computing core. Moreover, the fusion of the outputs from each computing core is equivalent to directly processing broadband signals. In the proof-of-concept experiment, two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated circuit. The bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is achieved. The wireless communication rate of 8 Gbit/s is also validated. Breaking the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture, we anticipate that the proposed APP will accelerate the development of powerful opto-electronic processors as critical support for applications such as satellite networks and intelligent driving.
Project description:SignificanceCurrent methods of producing optical phantoms are incapable of accurately capturing the wavelength-dependent properties of tissue critical for many optical modalities.AimWe aim to introduce a method of producing solid, inorganic phantoms whose wavelength-dependent optical properties can be matched to those of tissue over the wavelength range of 370 to 950 nm.ApproachThe concentration-dependent optical properties of 20 pigments were characterized and used to determine combinations that result in optimal fits compared to the target properties over the full spectrum. Phantoms matching the optical properties of muscle and nerve, the diffuse reflectance of pale and melanistic skin, and the chromophore concentrations of a computational skin model with varying oxygen saturation ( StO2 ) were made with this method.ResultsBoth optical property phantoms were found to accurately mimic their respective tissues' absorption and scattering properties across the entire spectrum. The diffuse reflectance phantoms were able to closely approximate skin reflectance regardless of skin type. All three computational skin phantoms were found to have emulated chromophore concentrations close to the model, with an average percent error for the StO2 of 4.31%.ConclusionsThis multipigment phantom platform represents a powerful tool for creating spectrally accurate tissue phantoms, which should increase the availability of standards for many optical techniques.
Project description:Targeted light delivery into biological tissue is needed in applications such as optogenetic stimulation of the brain and in vivo functional or structural imaging of tissue. These applications require very compact, soft, and flexible implants that minimize damage to the tissue. Here, we demonstrate a novel implantable photonic platform based on a high-density, flexible array of ultracompact (30 μm × 5 μm), low-loss (3.2 dB/cm at λ = 680 nm, 4.1 dB/cm at λ = 633 nm, 4.9 dB/cm at λ = 532 nm, 6.1 dB/cm at λ = 450 nm) optical waveguides composed of biocompatible polymers Parylene C and polydimethylsiloxane (PDMS). This photonic platform features unique embedded input/output micromirrors that redirect light from the waveguides perpendicularly to the surface of the array for localized, patterned illumination in tissue. This architecture enables the design of a fully flexible, compact integrated photonic system for applications such as in vivo chronic optogenetic stimulation of brain activity.
Project description:Metamaterials have the potential to create optical devices with new and diverse functionalities based on novel wave phenomena. Most practical optical systems require that the device properties be tightly controlled over a broad wavelength range. However, optical metamaterials are inherently dispersive, which limits operational bandwidths and leads to high absorption losses. Here, we show that deep-subwavelength inclusions can controllably tailor the dispersive properties of an established metamaterial structure thereby producing a broadband low-loss optical device with a desired response. We experimentally verify this by optimizing an array of nano-notch inclusions, which perturb the mode patterns and strength of the primary and secondary fishnet nanostructure resonances and give an optically thin mid-wave-infrared filter with a broad transmissive pass-band and near-constant group delay. This work outlines a powerful new strategy for realizing a wide range of broadband optical devices that exploit the unique properties of metamaterials.
Project description:Frequency-stabilized optical frequency combs have created many high-precision applications. Accurate timing, ultralow phase noise, and narrow linewidth are prerequisites for achieving the ultimate performance of comb-based systems. Ultrastable cavity-based comb-noise stabilization methods have enabled sub-10-15-level frequency instability. However, these methods are complex and alignment sensitive, and their use has been mostly confined to advanced metrology laboratories. Here, we have established a simple, compact, alignment-free, and potentially low-cost all-fiber photonics-based stabilization method for generating multiple ultrastable combs. The achieved performance includes 1-femtosecond timing jitter, few times 10-15-level frequency instability, and <5-hertz linewidth, rivalling those of cavity-stabilized combs. This method features flexibility in configuration: As a representative example, two combs were stabilized with 180-hertz repetition rate difference and ~1-hertz relative linewidth and could be used as an ultrastable, octave-spanning dual-comb spectroscopy source. The demonstrated method constitutes a mechanically robust and reconfigurable tool for generating multiple ultrastable combs suitable for field applications.
Project description:Metasurfaces impart phase discontinuities on impinging electromagnetic waves that are typically limited to 0-2π. Here, we demonstrate that multiresonant metasurfaces can break free from this limitation and supply arbitrarily large, tunable time delays over ultrawide bandwidths. As such, ultrathin metasurfaces can act as the equivalent of thick bulk structures by emulating the multiple geometric resonances of three-dimensional systems that originate from phase accumulation with effective material resonances implemented on the surface itself via suitable subwavelength meta-atoms. We describe a constructive procedure for defining the required sheet admittivities of such metasurfaces. Importantly, the proposed approach provides an exactly linear phase response so that broadband pulses can experience the desired group delay without any distortion of the pulse shape. We focus on operation in reflection by exploiting an antimatching condition, satisfied by interleaved electric and magnetic Lorentzian resonances in the surface admittivities, which completely zeroes out transmission through the metasurface. As a result, the proposed metasurfaces can perfectly reflect a broadband pulse imparting a prescribed group delay. The group delay can be tuned by modifying the implemented resonances, thus opening up diverse possibilities in the temporal applications of metasurfaces.
Project description:Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response. Our Deep Neural Network (DNN) could design compact (2.6 × 2.6 μm2) silicon-on-insulator (SOI)-based 1 × 2 power splitters with various target splitting ratios in a fraction of a second. This model is trained to minimize the reflection (to smaller than ~ -20 dB) while achieving maximum transmission efficiency above 90% and target splitting specifications. This approach paves the way for rapid design of integrated photonic components relying on complex nanostructures.
Project description:A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters and human errors. As a solution, we propose PointIso, the first point cloud based arbitrary-precision deep learning network to address this problem. It consists of attention based scanning step for segmenting the multi-isotopic pattern of 3D peptide features along with the charge, and a sequence classification step for grouping those isotopes into potential peptide features. PointIso achieves 98% detection of high-quality MS/MS identified peptide features in a benchmark dataset. Next, the model is adapted for handling the additional 'ion mobility' dimension and achieves 4% higher detection than existing algorithms on the human proteome dataset. Besides contributing to the proteomics study, our novel segmentation technique should serve the general object detection domain as well.