Trichannel Spin-Selective Metalenses.

Metalenses have the potential to revolutionize optical devices into the next generation of consumer devices. Through new inventive strategies, metalenses with advanced functionalities have been released to integrate multiple responses into a single flat device. Here, we design metalenses that are sensitive to the incident spin angular momentum to provide three distinct modes based on the handedness of the incident and transmitted light. Propagation phase is employed to encode a hyperbolic lens phase to the metalens, while geometric phase is exploited for additional spin-selective properties. We experimentally demonstrate two different metalenses: the co-polarized channels function as a standard metalens, while the cross-polarized channels (1) deflect and (2) introduce orbital angular momentum to the transmitted light. We experimentally characterize the metalenses and prove their use for spin-selective imaging of visible light. We envision that such trichannel metalenses could be employed in chiral bioimaging, optical computing, and computer vision.

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.

Reconfigurable Metasurface for Image Processing.

Optical Fourier transform-based processing is an attractive technique due to the fast processing times and large-data rates. Furthermore, it has recently been demonstrated that certain Fourier-based processors can be realized in compact form factors using flat optics. The flat optics, however, have been demonstrated as static filters where the operator is fixed, limiting the applicability of the approach. Here, we demonstrate a reconfigurable metasurface that can be dynamically tuned to provide a range of processing modalities including bright-field imaging, low-pass and high-pass filtering, and second-order differentiation. The dynamically tunable metasurface can be directly combined with standard coherent imaging systems and operates with a numerical aperture up to 0.25 and over a 60 nm bandwidth. The ability to dynamically control light in the wave vector domain, while doing so in a compact form factor, may open new doors to applications in microscopy, machine vision, and sensing.

Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks.

Optical machine learning offers advantages in terms of power efficiency, scalability and computation speed. Recently, an optical machine learning method based on Diffractive Deep Neural Networks (D2NNs) has been introduced to execute a function as the input light diffracts through passive layers, designed by deep learning using a computer. Here we introduce improvements to D2NNs by changing the training loss function and reducing the impact of vanishing gradients in the error back-propagation step. Using five phase-only diffractive layers, we numerically achieved a classification accuracy of 97.18% and 89.13% for optical recognition of handwritten digits and fashion products, respectively; using both phase and amplitude modulation (complex-valued) at each layer, our inference performance improved to 97.81% and 89.32%, respectively. Furthermore, we report the integration of D2NNs with electronic neural networks to create hybrid-classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D2NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network. Using a 5-layer phase-only D2NN jointly-optimized with a single fully-connected electronic layer, we achieved a classification accuracy of 98.71% and 90.04% for the recognition of handwritten digits and fashion products, respectively. Moreover, the input to the electronic network was compressed by >7.8 times down to 10×10 pixels. Beyond creating low-power and high-frame rate machine learning platforms, D2NN-based hybrid neural networks will find applications in smart optical imager and sensor design.

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.

A New Multivariate Optical Computing Microelement and Miniature Sensor for Spectroscopic Chemical Sensing in Harsh Environments: Design, Fabrication, and Testing.

Multivariate optical computing (MOC) is a compressed sensing technique with the ability to provide accurate spectroscopic compositional analysis in a variety of different applications to multiple industries. Indeed, recent developments have demonstrated the successful deployment of MOC sensors in downhole/well-logging environments to interrogate the composition of hydrocarbon and other chemical constituents in oil and gas reservoirs. However, new challenges have necessitated sensors that operate at high temperatures and pressures (up to 230 °C and 138 MPa) as well as even smaller areas that require the miniaturization of their physical footprint. To this end, this paper details the design, fabrication, and testing of a novel miniature-sized MOC sensor suited for harsh environments. A micrometer-sized optical element provides the active spectroscopic analysis. The resulting MOC sensor is no larger than two standard AAA batteries yet is capable of operating in high temperature and pressure conditions while providing accurate spectroscopic compositional analysis comparable to a laboratory Fourier transform infrared spectrometer.

Engineering Donor-Acceptor Heterostructure Metal-Organic Framework Crystals for Photonic Logic Computation.

Photonic materials use photons as information carriers and offer the potential for unprecedented applications in optical and optoelectronic devices. In this study, we introduce a new strategy for photonic materials using metal-organic frameworks (MOFs) as the host for the rational construction of donor-acceptor (D-A) heterostructure crystals. We have engineered a rich library of heterostructure crystals using the MOF NKU-111 as a host. NKU-111 is based upon an electron-deficient tridentate ligand (acceptor) that can bind to various electron-rich guests (donors). The resulting heterocrystals exhibit spatially segregated multi-color emission resulting from the guest-dependent charge-transfer (CT) emission. Spatially effective mono-directional energy transfer results from tuning the energy gradient between adjacent domains through the selection of donor guest molecules, which suggests potential applications in integrated optical circuit devices, for example, photonic diodes, on-chip signal processing, optical logic gates.

Hydrogen Sulfide Gas Detection via Multivariate Optical Computing.

Hydrogen-sulfide gas is a toxic, colorless gas with a pungent odor that occurs naturally as a decomposition by-product. It is critical to monitor the concentration of hydrogen sulfide. Multivariate optical computing (MOC) is a method that can monitor analytes while minimizing responses to interferences. MOC is a technique by which an analogue calculation is performed entirely in the optical domain, which simplifies instrument design, prevents the drift of a calibration, and increases the strength and durability of spectroscopic instrumentation against physical perturbation when used for chemical detection and identification. This paper discusses the detection of hydrogen-sulfide gas in the ultraviolet (UV) spectral region in the presence of interfering gaseous species. A laboratory spectroscopic measurement system was set up to acquire the UV spectra of H2S and interference gas mixtures in high-pressure/high-temperature (HPHT) conditions. These spectra were used to guide the design and fabrication of a multivariate optical element (MOE), which has an expected measurement relative accuracy of 3.3% for H2S, with a concentration in the range of 0⁻150 nmol/mL. An MOC validation system with the MOE was used to test three samples of H2S and mercaptans mixtures under various pressures, and the relative accuracy of H2S measurement was determined to be 8.05%.

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.

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.

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.

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.

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.

Prospects and applications of on-chip lasers.

Integrated silicon photonics has sparked a significant ramp-up of investment in both academia and industry as a scalable, power-efficient, and eco-friendly solution. At the heart of this platform is the light source, which in itself, has been the focus of research and development extensively. This paper sheds light and conveys our perspective on the current state-of-the-art in different aspects of application-driven on-chip silicon lasers. We tackle this from two perspectives: device-level and system-wide points of view. In the former, the different routes taken in integrating on-chip lasers are explored from different material systems to the chosen integration methodologies. Then, the discussion focus is shifted towards system-wide applications that show great prospects in incorporating photonic integrated circuits (PIC) with on-chip lasers and active devices, namely, optical communications and interconnects, optical phased array-based LiDAR, sensors for chemical and biological analysis, integrated quantum technologies, and finally, optical computing. By leveraging the myriad inherent attractive features of integrated silicon photonics, this paper aims to inspire further development in incorporating PICs with on-chip lasers in, but not limited to, these applications for substantial performance gains, green solutions, and mass production.

Data-Class-Specific All-Optical Transformations and Encryption.

Diffractive optical networks provide rich opportunities for visual computing tasks. Here, data-class-specific transformations that are all-optically performed between the input and output fields-of-view (FOVs) of a diffractive network are presented. The visual information of the objects is encoded into the amplitude (A), phase (P), or intensity (I) of the optical field at the input, which is all-optically processed by a data-class-specific diffractive network. At the output, an image sensor-array directly measures the transformed patterns, all-optically encrypted using the transformation matrices preassigned to different data classes, i.e., a separate matrix for each data class. The original input images can be recovered by applying the correct decryption key (the inverse transformation) corresponding to the matching data class, while applying any other key will lead to loss of information. All-optical class-specific transformations covering A → A, I → I, and P → I transformations using various image datasets are numerically demonstrated. The feasibility of this framework is also experimentally validated by fabricating class-specific I → I transformation diffractive networks and is successfully tested at different parts of the electromagnetic spectrum, i.e., 1550 nm and 0.75 mm wavelengths. Data-class-specific all-optical transformations provide a fast and energy-efficient method for image and data encryption, enhancing data security and privacy.

Mid-Infrared Variable Selection for Soil Organic Matter Fractions Based on Soil Model Systems and Permutation Importance Algorithm.

In this research, an attempt was made to classify soil samples according to the different fractions of soil organic matter (SOM) using model systems in which the ratio of the fractions of SOM is chemically mimicked. A mixture of starch and nicotinamide was used for the labile organic matter model, while a standard of humic acid was used for the stabile organic matter. Changing the threshold value in the selected ranges after a permutation importance algorithm is conducted using train models and test data set, a list of selected important wavelengths and their importance scores were obtained. Three regions for the classification of soil fractions within the estimated probability density function are most prominent: 800-1200 cm-1, 0.48-0.55; 1800-2000 cm-1, 0.52-0.62; and 2500-3200 cm-1, 0.48-0.62, where the first component represents the spectral range while the second component covers the range of the importance score. Obtained wavelength ranges indicate the importance of the aliphatic stretching and bending vibration region, as well as the total soil reflectance (mineral content) for the characterization of organic matter fractions. A comparative evaluation with literature data found that the obtained wavelengths have a potential for application in methods of proximal and remote detection/calibration of existing and development of new sensors for Advanced Spaceborne Thermal Emission and Reflection Radiometer satellites, specifically in the shortwave infrared and thermal infrared ranges.

An Introduction to Nonlinear Integrated Photonics Devices: Nonlinear Effects and Materials.

The combination of integrated optics technologies with nonlinear photonics, which has led to the growth of nonlinear integrated photonics, has also opened the way to groundbreaking new devices and applications. Here we introduce the main physical processes involved in nonlinear photonics applications, and we discuss the fundaments of this research area, starting from traditional second-order and third-order phenomena and going to ultrafast phenomena. The applications, on the other hand, have been made possible by the availability of suitable materials, with high nonlinear coefficients, and/or by the design of guided-wave structures, which can enhance the material's nonlinear properties. A summary of the most common nonlinear materials is presented, together with a discussion of the innovative ones. The discussion of fabrication processes and integration platforms is the subject of a companion article, also submitted for publication in this journal. There, several examples of nonlinear photonic integrated devices to be employed in optical communications, all-optical signal processing and computing, or quantum optics are shown, too. We aimed at offering a broad overview, even if, certainly, not exhaustive. We hope that the overall work could provide guidance for those who are newcomers to this field and some hints to the interested researchers for a more detailed investigation of the present and future development of this hot and rapidly growing field.

An Introduction to Nonlinear Integrated Photonics: Structures and Devices.

The combination of integrated optics technologies with nonlinear photonics, which has led to growth of nonlinear integrated photonics, has also opened the way to groundbreaking new devices and applications. In a companion paper also submitted for publication in this journal, we introduce the main physical processes involved in nonlinear photonics applications and discuss the fundaments of this research area. The applications, on the other hand, have been made possible by availability of suitable materials with high nonlinear coefficients and/or by design of guided-wave structures that can enhance a material's nonlinear properties. A summary of the traditional and innovative nonlinear materials is presented there. Here, we discuss the fabrication processes and integration platforms, referring to semiconductors, glasses, lithium niobate, and two-dimensional materials. Various waveguide structures are presented. In addition, we report several examples of nonlinear photonic integrated devices to be employed in optical communications, all-optical signal processing and computing, or in quantum optics. We aimed at offering a broad overview, even if, certainly, not exhaustive. However, we hope that the overall work will provide guidance for newcomers to this field and some hints to interested researchers for more detailed investigation of the present and future development of this hot and rapidly growing field.

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.

Parallel wave-based analog computing using metagratings.

Wave-based signal processing has witnessed a significant expansion of interest in a variety of science and engineering disciplines, as it provides new opportunities for achieving high-speed and low-power operations. Although flat optics desires integrable components to perform multiple missions, yet, the current wave-based computational metasurfaces can engineer only the spatial content of the input signal where the processed signal obeys the traditional version of Snell's law. In this paper, we propose a multi-functional metagrating to modulate both spatial and angular properties of the input signal whereby both symmetric and asymmetric optical transfer functions are realized using high-order space harmonics. The performance of the designed compound metallic grating is validated through several investigations where closed-form expressions are suggested to extract the phase and amplitude information of the diffractive modes. Several illustrative examples are demonstrated to show that the proposed metagrating allows for simultaneous parallel analog computing tasks such as first- and second-order spatial differentiation through a single multichannel structured surface. It is anticipated that the designed platform brings a new twist to the field of optical signal processing and opens up large perspectives for simple integrated image processing systems.