Computational spectrometers enable low-cost, in-situ, and rapid spectral analysis, with applications in chemistry, biology, and environmental science. Traditional filter-based spectral encoding approaches typically use filter arrays, complicating the manufacturing process and hindering device consistency. Here we propose a computational spectrometer spanning visible to mid-infrared by combining the Single-Spinning Film Encoder (SSFE) with a deep learning-based reconstruction algorithm. Optimization through particle swarm optimization (PSO) allows for low-correlation and high-complexity spectral responses under different polarizations and spinning angles. The spectrometer demonstrates single-peak resolutions of 0.5 nm, 2 nm, 10 nm, and dual-peak resolutions of 3 nm, 6 nm, 20 nm for the visible, near, and mid-infrared wavelength ranges. Experimentally, it shows an average MSE of 1.05 × 10⁻³ for narrowband spectral reconstruction in the visible wavelength range, with average center-wavelength and linewidth errors of 0.61 nm and 0.56 nm. Additionally, it achieves an overall 81.38% precision for the classification of 220 chemical compounds, showcasing its potential for compact, cost-effective spectroscopic solutions. Junren Wen, Weiming Shi and colleagues propose a computational spectrometer spanning visible to mid-infrared by integrating a Single-Spinning Film Encoder with deep learning-based reconstruction. This approach enables high-resolution spectral analysis and accurate chemical classification.

Modern computational technologies are gradually encountering significant limitations, driving a shift toward alternative paradigms such as optical computing. In this study, novel all-optical combinational logic units based on diffractive neural networks (D2NNs) were introduced, which were designed to perform high-order logical operations efficiently and swiftly with the adoption of only two modulation layers. This innovative design exhibits increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging the transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for a practical information transmission system. Furthermore, we revealed a pioneering application of the device in optical time division multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations demonstrate the potential of the model as a foundational technology for future optical computing architectures, which paves the way toward more sustainable and efficient optical data processing platforms.

The development of colored electrodes has significant implications for various applications, offering an enhanced blend of aesthetics and functionality across multiple fields. Traditional approaches to achieving this, such as altering material composition, modifying carrier density, and applying surface treatments or micro-nano structures, face challenges in terms of complexity and manufacturing costs. This study introduces an innovative, high-conductivity electrode design capable of flexibly controlling the reflective spectra. We have successfully fabricated various flexible colored devices characterized by high brightness and saturation through a room-temperature preparation process, exhibiting remarkably low electrical resistance of approximately 300 m Omega/sq. The construction of the asymmetrical Fabry-Perot resonance cavity, composed of a four-layer compact film stack, enables the enhancement or suppression of specific light wavelengths, facilitating efficient spectral filtering with conductive dielectric and metals. Moreover, the electrode exhibits remarkable bending properties, maintaining conductivity and color integrity even after 5000 bending cycles and at high curvatures (up to 90 m(-1)), a feature attributed to its ultrathin structure (<600 nm). Our findings showcase the electrode's potential in a wide range of electronic and optical applications, including electromagnetic shielding/camouflage and photovoltaic devices, as demonstrated in our research.

Optical Diffraction Neural Networks (DNNs), a subset of Optical Neural Networks (ONNs), show promise in mirroring the prowess of electronic networks. This study introduces the Hybrid Diffraction Neural Network (HDNN), a novel architecture that incorporates matrix multiplication into DNNs, synergizing the benefits of conventional ONNs with those of DNNs to surmount the modulation limitations inherent in optical diffraction neural networks. Utilizing a singular phase modulation layer and an amplitude modulation layer, the trained neural network demonstrated remarkable accuracies of 96.39 and 89% in digit recognition tasks in simulation and experiment, respectively. Additionally, we develop the Binning Design (BD) method, which effectively mitigates the constraints imposed by sampling intervals on diffraction units, substantially streamlining experimental procedures. Furthermore, we propose an On-chip HDNN that not only employs a beam-splitting phase modulation layer for enhanced integration level but also significantly relaxes device fabrication requirements, replacing metasurfaces with relief surfaces designed by 1-bit quantization. Besides, we conceptualized an all-optical HDNN-assisted lesion detection network, achieving detection outcomes that were 100% aligned with simulation predictions. This work not only advances the performance of DNNs but also streamlines the path toward industrial optical neural network production.

Traditional spectral imaging methods are constrained by the time-consuming scanning process, limiting the application in dynamic scenarios. One-shot spectral imaging based on reconstruction has been a hot research topic recently and the primary challenges still lie in both efficient fabrication techniques suitable for mass production and the high-speed, high-accuracy reconstruction algorithm for real-time spectral imaging. In this study, we introduce an innovative on-chip real-time hyperspectral imager that leverages nanophotonic film spectral encoders and a Massively Parallel Network (MP-Net), featuring a 4 * 4 array of compact, all-dielectric film units for the micro-spectrometers. Each curved nanophotonic film unit uniquely modulates incident light across the underlying 3 * 3 CMOS image sensor (CIS) pixels, enabling a high spatial resolution equivalent to the full CMOS resolution. The implementation of MP-Net, specially designed to address variability in transmittance and manufacturing errors such as misalignment and non-uniformities in thin film deposition, can greatly increase the structural tolerance of the device and reduce the preparation requirement, further simplifying the manufacturing process. Tested in varied environments on both static and moving objects, the real-time hyperspectral imager demonstrates the robustness and high-fidelity spatial-spectral data capabilities across diverse scenarios. This on-chip hyperspectral imager represents a significant advancement in real-time, high-resolution spectral imaging, offering a versatile solution for applications ranging from environmental monitoring, remote sensing to consumer electronics.

Optical Diffraction Neural Networks (DNNs), a subset of Optical Neural Networks (ONNs), show promise in mirroring the prowess of electronic networks. This study introduces the Hybrid Diffraction Neural Network (HDNN), a novel architecture that incorporates matrix multiplication into DNNs, synergizing the benefits of conventional ONNs with those of DNNs to surmount the modulation limitations inherent in optical diffraction neural networks. Utilizing a singular phase modulation layer and an amplitude modulation layer, the trained neural network demonstrated remarkable accuracies of 96.39 recognition tasks in simulation and experiment, respectively. Additionally, we develop the Binning Design (BD) method, which effectively mitigates the constraints imposed by sampling intervals on diffraction units, substantially streamlining experimental procedures. Furthermore, we propose an on-chip HDNN that not only employs a beam-splitting phase modulation layer for enhanced integration level but also significantly relaxes device fabrication requirements, replacing metasurfaces with relief surfaces designed by 1-bit quantization. Besides, we conceptualized an all-optical HDNN-assisted lesion detection network, achieving detection outcomes that were 100 simulation predictions. This work not only advances the performance of DNNs but also streamlines the path towards industrial optical neural network production.

Ultralow refractive index films can reduce the reflection loss of light propagating between different media, which is crucial for enhancing the performance and efficiency of optical components. This work presents an approach to the production of ultralow refractive index thin films based on magnetron cosputtering and subsequent selective chemical etching, where a nanoporous silicon dioxide film with a variable refractive index is fabricated by the sacrificial method of forming random holes with a size less than 100 nm. The effective refractive index can be adjusted from 1.1 to 1.46 by simply controlling the sputtering power ratio of the silicon target to the aluminum target. The antireflection properties of ultralow refractive index films have been verified, and their process reproducibility and environmental reliability have been evaluated. The results demonstrate the efficacy of this technique as a viable solution for large-area, low-cost, and high-performance fabrication of ultralow refractive index coatings, which have great potential for the preparation of industrial-scale antireflective films.

In the 3-5 & mu;m mid-wave infrared (MWIR) atmospheric transparency window region, infrared detectors are widely applied in spectral imaging, remote sensing and other fields, and their rapid development has put forward higher requirements for infrared optical windows. Common mid-wave infrared optical materials such as germanium and zinc sulfide have poor mechanical properties, causing them difficult to meet various harsh environments. In this paper, a 3-layer film stack is proposed to achieve high transmission and high hardness by radio-frequency reactive magnetron sputtering with the single aluminum target. Two types of AlN/ Al2O3/AlN stack with top AlN thicknesses of 50 and 100 nm are studied to realize the best optical and mechanical characteristics. The average transmission increases up to 94.0% for 3-5 & mu;m with the double-side AlN/Al2O3/AlN stacks on the silicon substrate, while the hardness is significantly improved to-16.6 GPa for both types. After annealing at 500 degrees C and 750 degrees C for 6 h in a vacuum environment, the spectra of the two film stacks move towards the shorter wavelength slightly, and the hardness of both are obviously enhanced. The highest hardness of the film system reaches-20.8 GPa, making it far more resistant to sand erosion than ordinary antireflection films. The result shows that this film stack can withstand high temperature and gravel impact tests, meeting the service requirements of the harsh environment of the MWIR optical system.

Colorful radiative coolers (CRCs) can be widely applied for energy sustainability especially and meet aesthetic purposes simultaneously. Here, we propose a high-efficiency CRC based on thin film stacks and engineered diffuse reflection unit, which brings out 7.1 °C temperature difference compared with ambient under ~ 700 W·m −2 solar irradiation. Different from analogous schemes, the proposed CRCs produce vivid colors by diffuse reflection and rest of the incident light is specular-reflected without being absorbed. Adopting the structure of TiO 2 /SiO 2 multilayer stack, the nanophotonic radiative cooler shows extra low absorption across the solar radiation waveband. Significant radiative cooling performance can be achieved with the emissivity reaching 95.6% in the atmosphere transparent window (8–13 μm). Moreover, such CRC can be fabricated on flexible substrates, facilitating various applications such as the thermal management of cars or wearables. In conclusion, this work demonstrates a new approach for color display with negligible solar radiation absorption and paves the way for prominent radiative cooling.

Medium Wave Infrared(MWIR) light(@ 3 similar to 5 mu m) has high transmittance in the atmosphere. MWIR detectors have been widely used in the fields of infrared guidance, infrared imaging,gas detection, and space remote sensing, especially in military application prospects. High-temperature infrared radiation is generated by high-speed aircraft and missiles due to air friction and jet exhaust, which is often detected by infrared detectors. Compared with short-wave infrared, radar, and laser detectors, MWIR detectors possess higher sensitivity. In addition,the absorption lines of massive gas molecules concentrate in the mid-wave infrared band, thus it is often used for gas detection and industrial analysis. In practical applications, detector windows and lens of MWIR systems will be deposited with Anti- Reflection (AR) coating to reduce the energy loss during propagation and improve the imaging quality by eliminating stray light. However, coating materials are typically soft. Anti-reflection coating materials commonly used in mid-wave infrared band e.g. Ge, ZnS, MgF2, have excellent optical properties,but poor mechanical properties. To improve the optical performance of the systems and enhance the resistance to various harsh working environments, the optical windows and the surfaces of the films are usually coated with high hardness film for protection. This article describes the design, material selection and application of the MWIR antireflection hard coating. A summary of common hard film materials including the metal oxides, nitrides, and carbides are presented with the optical and mechanical properties. And the grain size strengthening, Koehler theory, nanocomposites methods are outlined to improve the film hardness. The first section illustrates the design theory of anti-reflection film, solutions to the high stress issue of hard protective film, and the design strategy of hard anti-reflection film. The second part introduces representative hard film materials in the mid-wave infrared band. The relationship between substrate temperature,gas flow rate, deposition rate, film stack design and mechanical properties of the films during the preparation process is analyzed. Among these hard coating materials,metal oxide films such as Al2O3,Y2O3,and HfO2 own excellent comprehensive properties and can be used as optical windows or protective films in some industrial applications. Differently,elements such as carbon and nitrogen have the advantages of small atomic bond length and thus strong chemical bond. For example , Diamond-Like Carbon(DLC)and BN films posses the advantages of extremely high hardness and relatively high light transmittance in the mid-wave infrared band,which have great potential in MWIR military applications. The third part of this paper summarizes the effective methods to improve the hardness of the films including grain boundary strengthening, Koehler theory,and doping other elements to form nanocomposite materials. Koehler

Wavelength‐selective light trapping has been widely applied in fields such as energy utilization, optical sensing, optical imaging, and so forth. Though metasurfaces have exhibited to efficiently trap the light for diverse optical responses with specific configurations, the manufacturing costs and processing precision both limit the extensive applications. Here, a compact optical coating to trap light either at a single wavelength or across a broadband within a nanometer‐thick single‐layer metallic coating based on mirror|phase tuning layer|absorptive (MPA) and MPA|anti‐reflection coating stacks is proposed. The intermediate phase‐tuning dielectric provides a specific phase shift for the cavity to achieve destructive or constructive interference for ≈100% absorption or reflection correspondingly by switching from ultrahigh‐index dielectric behavior to epsilon‐near‐zero material behavior. And the additional dielectric is introduced to reduce the increasing reflection for the final broadband absorption with a thicker absorptive metal. Furthermore, the efficient color‐preserving solar–thermal conversion based on this compact coating (4‐layer) with the entire thickness of ≈300 nm is demosntrated experimentally. Various perceived colors are produced by simply changing the thickness of the top dielectric layer with the efficient solar–thermal conversion remained, verified by the temperature difference between the fabricated device and the dye‐based plastic exceeding 13 °C with the solar irradiance ≈850 W m −2 .

The beam splitter is one of the basic optical components. Traditional beam splitters use bulky cubes or plates to realize beam splitting and thereby can hardly be used in a compact optical system. Recently, the beam splitter based on phase gradient metasurfaces has been proposed to split beam with a compact size. However, it is facing challenges on the efficiency and manufacturability. Herein, a beam splitter based on single‐sized flat dielectric metagrating for arbitrary polarizations is proposed. The diffraction efficiency of the fabricated beam splitter reaches as high as 80% for both TM and TE polarizations at peak wavelength. Moreover, it has a broad working wavelength range (455–550 nm) at visible wavelength where the diffraction efficiency is much higher than 60%. Apart from the broadband and high diffraction efficiency, the steering angle can be simply adjusted by varying the periodicity. Splitting light with a flat metagrating enriches the field of flat photonics, opening up new possibilities in applications such as augment reality displays, imaging systems, compact spectrometers, and lidars.

Conventional benchtop spectrometers with bulky dispersive optics and long optical path lengths display limitations where the significance of miniaturization, real-time detection, and low cost transcend the ultrafine resolution and wide spectral range. Here, we demonstrate a miniaturized all-dielectric ultracompact film spectrometer based on deep learning working in the single-shot mode. The scheme employs 16 spectral encoders with simple five-layer film stacks where merely the thickness of the intermediate high-index modulation layer is varied to realize unique encoded transmission spectra. Structural parameters as well as transmission spectra of the filters are predesigned to guarantee weak correlation and highly efficient encoding. Leveraging a trained reconstruction network, the absolute spectra of various nonluminous samples are successfully reconstructed excluding the emitting spectrum of the light source and the spectral response of the detector. The remarkable reconstructed spectral imaging result for the color board is presented and the reconstructed spectra match well with the measured ones for different patches using the identical network. We utilized the least number of spectral encoders ever since to guarantee efficient encoding, along with the single thickness-variant modulation layer, which shows potential for mass, rapid, large-area production by combining deposition with nanoimprint. Instead of the synthetic Gaussian line shape spectra, a training dataset composed of diverse spectrum types is adopted to achieve fine generalization of the trained reconstruction network. In addition, by retraining the neural network, the reconstruction network is modified to fit for the actual filter functions of the spectral encoders, thus better reconstruction performance. The proposed miniaturized spectrometer has great prospects in the fields of consumer electronics, environmental monitoring, and disaster prevention.