Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using a Deep Learning-Designed Diffractive Polarization Transformer

Controlled synthesis of optical fields having nonuniform polarization distributions presents a challenging task. Here, a universal polarization transformer is demonstrated that can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs). This framework comprises 2D arrays of linear polarizers positioned between isotropic diffractive layers, each containing tens of thousands of diffractive features with optimizable transmission coefficients. After its deep learning-based training, this diffractive polarization transformer can successfully implement Ni No = 10 000 different spatially-encoded polarization scattering matrices with negligible error, where Ni and No represent the number of pixels in the input and output FOVs, respectively. This universal polarization transformation framework is experimentally validated in the terahertz spectrum by fabricating wire-grid polarizers and integrating them with 3D-printed diffractive layers to form a physical polarization transformer. Through this set-up, an all-optical polarization permutation operation of spatially-varying polarization fields is demonstrated, and distinct spatially-encoded polarization scattering matrices are simultaneously implemented between the input and output FOVs of a compact diffractive processor. This framework opens up new avenues for developing novel devices for universal polarization control and may find applications in, e.g., remote sensing, medical imaging, security, material inspection, and machine vision.

Photonic Crystal Flip-Flops: Recent Developments in All Optical Memory Components

This paper reviews recent advancements in all-optical memory components, particularly focusing on various types of all-optical flip-flops (FFs) based on photonic crystal (PC) structures proposed in recent years. PCs, with their unique optical properties and engineered structures, including photonic bandgap control, enhanced light-matter interaction, and compact size, make them especially suitable for optical FFs. The study explores three key materials, silicon, chalcogenide glass, and gallium arsenide, known for their high refractive index contrast, compact size, hybrid integration capability, and easy fabrication processes. Furthermore, these materials exhibit excellent compatibility with different technologies like CMOS and fiber optics, enhancing their versatility in various applications. The structures proposed in the research leverage mechanisms such as waveguides, ring resonators, scattering rods, coupling rods, edge rods, switches, resonant cavities, and multi-mode interference. The paper delves into crucial properties and parameters of all-optical FFs, including response time, contrast ratio, and operating wavelength. Optical FFs possess significant advantages, such as high speed, low power consumption, and potential for integration, making them a promising technology for advancing optical computing and optical memory systems.

Bright-Field and Edge-Enhanced Imaging Using an Electrically Tunable Dual-Mode Metalens

The imaging of microscopic biological samples faces numerous difficulties due to their small feature sizes and low-amplitude contrast. Metalenses have shown great promise in bioimaging as they have access to the complete complex information, which, alongside their extremely small and compact footprint and potential to integrate multiple functionalities into a single device, allow for miniaturized microscopy with exceptional features. Here, we design and experimentally realize a dual-mode metalens integrated with a liquid crystal cell that can be electrically switched between bright-field and edge-enhanced imaging on the millisecond scale. We combine the concepts of geometric and propagation phase to design the dual-mode metalens and physically encode the required phase profiles using hydrogenated amorphous silicon for operation at visible wavelengths. The two distinct metalens phase profiles include (1) a conventional hyperbolic metalens for bright-field imaging and (2) a spiral metalens with a topological charge of +1 for edge-enhanced imaging. We demonstrate the focusing and vortex generation ability of the metalens under different states of circular polarization and prove its use for biological imaging. This work proves a method for in vivo observation and monitoring of the cell response and drug screening within a compact form factor.

Polarization-Dependent Metasurface Enables Near-Infrared Dual-Modal Single-Pixel Sensing

Infrared single-pixel sensing with the two most representative modes, bright-field imaging and edge-enhanced imaging, has great application potential in biomedical diagnosis and defect inspection. Building a multifunctional and miniature optical computing device for infrared single-pixel sensing is extremely intriguing. Here, we propose and validate a dual-modal device based on a well-designed metasurface, which enables near-infrared bright-field and edge-enhanced single-pixel imaging. By changing the polarization of the incident beam, these two different modes can be switched. Simulations validate that our device can achieve high-fidelity dual-modal single-pixel sensing at 0.9 μm with certain noise robustness. We also investigate the generalization of our metasurface-based device and validate that different illumination patterns are applied to our device. Moreover, these output images by our device can be efficiently utilized for biomedical image segmentation. We envision this novel device may open a vista in dual-modal infrared single-pixel sensing.

Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser

Excitable optoelectronic devices represent one of the key building blocks for implementation of artificial spiking neurons in neuromorphic (brain-inspired) photonic systems. This work introduces and experimentally investigates an opto-electro-optical (O/E/O) artificial neuron built with a resonant tunnelling diode (RTD) coupled to a photodetector as a receiver and a vertical cavity surface emitting laser as a transmitter. We demonstrate a well-defined excitability threshold, above which the neuron produces optical spiking responses with characteristic neural-like refractory period. We utilise its fan-in capability to perform in-device coincidence detection (logical AND) and exclusive logical OR (XOR) tasks. These results provide first experimental validation of deterministic triggering and tasks in an RTD-based spiking optoelectronic neuron with both input and output optical (I/O) terminals. Furthermore, we also investigate in simulation the prospects of the proposed system for nanophotonic implementation in a monolithic design combining a nanoscale RTD element and a nanolaser; therefore demonstrating the potential of integrated RTD-based excitable nodes for low footprint, high-speed optoelectronic spiking neurons in future neuromorphic photonic hardware.

Partitionable High-Efficiency Multilayer Diffractive Optical Neural Network

A partitionable adaptive multilayer diffractive optical neural network is constructed to address setup issues in multilayer diffractive optical neural network systems and the difficulty of flexibly changing the number of layers and input data size. When the diffractive devices are partitioned properly, a multilayer diffractive optical neural network can be constructed quickly and flexibly without readjusting the optical path, and the number of optical devices, which increases linearly with the number of network layers, can be avoided while preventing the energy loss during propagation where the beam energy decays exponentially with the number of layers. This architecture can be extended to construct distinct optical neural networks for different diffraction devices in various spectral bands. The accuracy values of 89.1% and 81.0% are experimentally evaluated for MNIST database and MNIST fashion database and show that the classification performance of the proposed optical neural network reaches state-of-the-art levels.

Optical Computing: Status and Perspectives

For many years, optics has been employed in computing, although the major focus has been and remains to be on connecting parts of computers, for communications, or more fundamentally in systems that have some optical function or element (optical pattern recognition, etc.). Optical digital computers are still evolving; however, a variety of components that can eventually lead to true optical computers, such as optical logic gates, optical switches, neural networks, and spatial light modulators have previously been developed and are discussed in this paper. High-performance off-the-shelf computers can accurately simulate and construct more complicated photonic devices and systems. These advancements have developed under unusual circumstances: photonics is an emerging tool for the next generation of computing hardware, while recent advances in digital computers have empowered the design, modeling, and creation of a new class of photonic devices and systems with unparalleled challenges. Thus, the review of the status and perspectives shows that optical technology offers incredible developments in computational efficiency; however, only separately implemented optical operations are known so far, and the launch of the world's first commercial optical processing system was only recently announced. Most likely, the optical computer has not been put into mass production because there are still no good solutions for optical transistors, optical memory, and much more that acceptance to break the huge inertia of many proven technologies in electronics.

Plasmonics for Telecommunications Applications

Plasmonic materials, when properly illuminated with visible or near-infrared wavelengths, exhibit unique and interesting features that can be exploited for tailoring and tuning the light radiation and propagation properties at nanoscale dimensions. A variety of plasmonic heterostructures have been demonstrated for optical-signal filtering, transmission, detection, transportation, and modulation. In this review, state-of-the-art plasmonic structures used for telecommunications applications are summarized. In doing so, we discuss their distinctive roles on multiple approaches including beam steering, guiding, filtering, modulation, switching, and detection, which are all of prime importance for the development of the sixth generation (6G) cellular networks.

OptCAM: An ultra-fast all-optical architecture for DNA variant discovery

Nowadays, the accelerated expansion of genetic data challenges speed of current DNA sequence alignment algorithms due to their electrical implementations. Essential needs of an efficient and accurate method for DNA variant discovery demand new approaches for parallel processing in real time. Fortunately, photonics, as an emerging technology in data computing, proposes optical correlation as a fast similarity measurement algorithm; while complexity of existing local alignment algorithms severely limits their applicability. Hence, in this paper, employing optical correlation for global alignment, we present an optical processing approach for local DNA sequence alignment to benefit both high-speed processing and operational parallelism, inherently exist in optics. The proposed method, named as OptCAM, utilizes amplitude and wavelength of the optical signals, to accurately locate mutations through three main procedures. Furthermore, an all-optical implementation of the OptCAM method is proposed consisting of three units, corresponding to the three OptCAM procedures. Performing considerably fast processes by passing optical signals through high-throughput photonic devices, OptCAM avoids various limitations of electrical implementations. Accuracy and efficiency of the OptCAM method and its optical implementation are validated through numerical simulation by a gold standard simulation benchmark. The results indicate the proposed method is significantly faster than its electrical counterparts, in both single node and grid computation.

Asymmetric Versus Symmetric Filter Wheels and Associated Processing Algorithms: Results from Asynchronous Fluorescence Imaging Photometer Measurements of Phytoplankton

The use of rotating filter wheels is common in photometric applications. Traditional filter wheel designs typically exhibit a number of filter openings spaced evenly about the circumference of the wheel. In this work we examine a number of shortcomings of this traditional filter design in measurements of phytoplankton fluorescence made with our fluorescence imaging photometer (FIP). We present an alternative asymmetric wheel design that offers a number of advantages over the traditional design as well as a new processing algorithm designed to accommodate convolution of signals from adjacent channels inherent in measurements collected with the asymmetric design. This approach eliminates the need for a separate signal to establish timing and wheel position, unambiguously establishes filter order even when the direction of rotation is unknown, allows for better estimates of signal baseline, and is more resilient to effects of vibration and other dynamic processes that could occur on the time scale of wheel rotation. We demonstrate performance improvements for phytoplankton fluorescence measurements associated with the new wheel design and algorithm compared with previously published methods using the FIP. Both the improved image processing algorithm and filter wheel design were found to reduce noise in our measurements significantly.

Fluorescence Excitation Spectroscopy for Phytoplankton Species Classification Using an All-Pairs Method: Characterization of a System with Unexpectedly Low Rank

An all-pairs method is used to analyze phytoplankton fluorescence excitation spectra. An initial set of nine phytoplankton species is analyzed in pairwise fashion to select two optical filter sets, and then the two filter sets are used to explore variations among a total of 31 species in a single-cell fluorescence imaging photometer. Results are presented in terms of pair analyses; we report that 411 of the 465 possible pairings of the larger group of 31 species can be distinguished using the initial nine-species-based selection of optical filters. A bootstrap analysis based on the larger data set shows that the distribution of possible pair separation results based on a randomly selected nine-species initial calibration set is strongly peaked in the 410-415 pair separation range, consistent with our experimental result. Further, the result for filter selection using all 31 species is also 411 pair separations; The set of phytoplankton fluorescence excitation spectra is intuitively high in rank due to the number and variety of pigments that contribute to the spectrum. However, the results in this report are consistent with an effective rank as determined by a variety of heuristic and statistical methods in the range of 2-3. These results are reviewed in consideration of how consistent the filter selections are from model to model for the data presented here. We discuss the common observation that rank is generally found to be relatively low even in many seemingly complex circumstances, so that it may be productive to assume a low rank from the beginning. If a low-rank hypothesis is valid, then relatively few samples are needed to explore an experimental space. Under very restricted circumstances for uniformly distributed samples, the minimum number for an initial analysis might be as low as 8-11 random samples for 1-3 factors.

On-chip photonic Fourier transform with surface plasmon polaritons

The Fourier transform (FT), a cornerstone of optical processing, enables rapid evaluation of fundamental mathematical operations, such as derivatives and integrals. Conventionally, a converging lens performs an optical FT in free space when light passes through it. The speed of the transformation is limited by the thickness and the focal length of the lens. By using the wave nature of surface plasmon polaritons (SPPs), here we demonstrate that the FT can be implemented in a planar configuration with a minimal propagation distance of around 10 μm, resulting in an increase of speed by four to five orders of magnitude. The photonic FT was tested by synthesizing intricate SPP waves with their Fourier components. The reduced dimensionality in the minuscule device allows the future development of an ultrafast on-chip photonic information processing platform for large-scale optical computing.