A Pearl Spectrometer

Advances in Miniaturized Computational Spectrometers

Miniaturized computational spectrometers have emerged as a promising strategy for miniaturized spectrometers, which breaks the compromise between footprint and performance in traditional miniaturized spectrometers by introducing computational resources. They have attracted widespread attention and a variety of materials, optical structures, and photodetectors are adopted to fabricate computational spectrometers with the cooperation of reconstruction algorithms. Here, a comprehensive review of miniaturized computational spectrometers, focusing on two crucial components: spectral encoding and reconstruction algorithms are provided. Principles, features, and recent progress of spectral encoding strategies are summarized in detail, including space-modulated, time-modulated, and light-source spectral encoding. The reconstruction algorithms are classified into traditional and deep learning algorithms, and they are carefully analyzed based on the mathematical models required for spectral reconstruction. Drawing from the analysis of the two components, cooperations between them are considered, figures of merits for miniaturized computational spectrometers are highlighted, optimization strategies for improving their performance are outlined, and considerations in operating these systems are provided. The application of miniaturized computational spectrometers to achieve hyperspectral imaging is also discussed. Finally, the insights into the potential future applications and developments of computational spectrometers are provided.

Compact speckle spectrometer using femtosecond laser-induced double-sided surface nanostructures

The utilization of light scattering in disordered media has shown promise in the design of highly sensitive speckle spectrometers. Significant advances have been made in the research of all-fiber speckle spectrometers, and various planar scattering media have also garnered the attention of many researchers. In this study, we designed a compact speckle spectrometer employing a femtosecond laser to induce double-sided nanostructures on a quartz glass as a scattering medium. Once the transmission matrix is calibrated, the spectrum can be reconstructed over a bandwidth of 100 nm, achieving a spectral resolution of 0.1 nm. A 5 pm spectral resolution has been demonstrated by integrating a neural network to recognize speckle patterns in 100 pm bandwidths at 1500, 1550, and 1600 nm. By combining ResNet-50 and GRU, a simulated continuous spectrum spinning a bandwidth of 2 nm can be accurately reconstructed. This innovative, compact spectrometer features low cost, small size, simple preparation, and repeatability.

i-PHAOS: An Overview with an Open-Source Collaborative Database on Miniaturized Integrated Spectrometers

On-chip spectrometers are increasingly becoming tools that might help in everyday life needs. The possibility offered by several available integration technologies and materials to be used to miniaturize spectrometers has led to a plethora of very different devices, that in principle can be compared according to their metrics. Having access to a reference database can help in selecting the best-performing on-chip spectrometers and being up to date in terms of standards and developments. In this paper, an overview of the most relevant publications available in the literature on miniaturized spectrometers is reported and a database is provided as an open-source project to which researchers can have access and participate in order to improve the share of knowledge in the interested scientific community.

Miniaturized on-chip spectrometer enabled by electrochromic modulation

Miniaturized on-chip spectrometers with small footprints, lightweight, and low cost are in great demand for portable optical sensing, lab-on-chip systems, and so on. Such miniaturized spectrometers are usually based on engineered spectral response units and then reconstruct unknown spectra with algorithms. However, due to the limited footprints of computational on-chip spectrometers, the recovered spectral resolution is limited by the number of integrated spectral response units/filters. Thus, it is challenging to improve the spectral resolution without increasing the number of used filters. Here we present a computational on-chip spectrometer using electrochromic filter-based computational spectral units that can be electrochemically modulated to increase the efficient sampling number for higher spectral resolution. These filters are directly integrated on top of the photodetector pixels, and the spectral modulation of the filters results from redox reactions during the dual injection of ions and electrons into the electrochromic material. We experimentally demonstrate that the spectral resolution of the proposed spectrometer can be effectively improved as the number of applied voltages increases. The average difference of the peak wavelengths between the reconstructed and the reference spectra decreases from 1.61 nm to 0.29 nm. We also demonstrate the proposed spectrometer can be worked with only four or two filter units, assisted by electrochromic modulation. In addition, we also demonstrate that the electrochromic filter can be easily adapted for hyperspectral imaging, due to its uniform transparency. This strategy suggests a new way to enhance the performance of miniaturized spectrometers with tunable spectral filters for high resolution, low-cost, and portable spectral sensing, and would also inspire the exploration of other stimulus responses such as photochromic and force-chromic, etc, on computational spectrometers.

Ultra-wide-angle multispectral narrow-band absorber for infrared spectral reconstruction

This paper presents the design of an ultra-wide-angle multispectral narrow-band absorber for reconstructing infrared spectra. The absorber offers several advantages, including polarization sensitivity, robustness against structural wear, wide azimuthal angle coverage, high narrow-band absorption, and adjustable working wavelength. To accomplish infrared spectrum reconstruction, an absorber is employed as a spectral sampling channel, eliminating the influence of slits or complex optical splitting elements in spectral imaging technology. Additionally, we propose using a truncation regularization algorithm based on the design matrix singular value ratio, namely IReg, which can enable high-precision spectral reconstruction under largely disturbed environments. The results demonstrate that, even when the number of absorption spectrum curve is reduced to a range of 1/2 to 1/3, high-precision spectral reconstruction is achievable for both flat and high-energy steep mid- and long-infrared spectral targets, while effectively accomplishing data dimension reduction.

Miniature computational spectrometer with a plasmonic nanoparticles-in-cavity microfilter array

Optical spectrometers are essential tools for analysing light‒matter interactions, but conventional spectrometers can be complicated and bulky. Recently, efforts have been made to develop miniaturized spectrometers. However, it is challenging to overcome the trade-off between miniaturizing size and retaining performance. Here, we present a complementary metal oxide semiconductor image sensor-based miniature computational spectrometer using a plasmonic nanoparticles-in-cavity microfilter array. Size-controlled silver nanoparticles are directly printed into cavity-length-varying Fabry‒Pérot microcavities, which leverage strong coupling between the localized surface plasmon resonance of the silver nanoparticles and the Fabry‒Pérot microcavity to regulate the transmission spectra and realize large-scale arrayed spectrum-disparate microfilters. Supported by a machine learning-based training process, the miniature computational spectrometer uses artificial intelligence and was demonstrated to measure visible-light spectra at subnanometre resolution. The high scalability of the technological approaches shown here may facilitate the development of high-performance miniature optical spectrometers for extensive applications.

Chiral Pearlescent Cellulose Nanocrystals Films with Broad-Range Tunable Optical Properties for Anti-Counterfeiting Applications

Pearlescent materials are of technological importance in a diverse array of industries from cosmetics to premium paints; however, chiral pearlescent materials remain unexplored. Here, chiral pearlescent films with on-demand iridescence and metallic appearance are simply organized by leveraging vertical pressure to direct the self-assembly of cellulose nanocrystals. The films are formed with a bilayer planar anchored left-handed chiral nematic architecture, in which the bottom layer is featured with a vertical gradient pitch, and the top layer is featured with a uniform pitch. Simultaneous reflection of the rainbow colors and an on-demand color of left-handed polarized light with angle-dependent wavelength and polarization state accounts for the unique optical phenomenon based on experimental observation and theoretical analysis. Such chiroptical property can be readily tuned with architectural design, enabling reproducible optical appearance with high fidelity. Bringing the pearlescence, iridescence, and specular reflection together endows cellulose nanocrystal films with rich and tunable chiroptical properties that can be used for anti-counterfeiting applications. The current work marks the beginning of chiral pearlescent materials from renewable resources, while the pressure-directed self-assembly provides a step toward scalable production.

Review of Miniaturized Computational Spectrometers

Spectrometers are key instruments in diverse fields, notably in medical and biosensing applications. Recent advancements in nanophotonics and computational techniques have contributed to new spectrometer designs characterized by miniaturization and enhanced performance. This paper presents a comprehensive review of miniaturized computational spectrometers (MCS). We examine major MCS designs based on waveguides, random structures, nanowires, photonic crystals, and more. Additionally, we delve into computational methodologies that facilitate their operation, including compressive sensing and deep learning. We also compare various structural models and highlight their unique features. This review also emphasizes the growing applications of MCS in biosensing and consumer electronics and provides a thoughtful perspective on their future potential. Lastly, we discuss potential avenues for future research and applications.

Computational hyperspectral devices based on quasi-random metasurface supercells

Computational hyperspectral devices that use artificial filters have shown promise as compact spectral devices. However, the current designs are restricted by limited types and geometric parameters of unit cells, resulting in a high cross-correlation between the transmission spectra. This limitation prevents the fulfillment of the requirement for compressed-sensing-based spectral reconstruction. To address this challenge, we proposed and simulated a novel design for computational hyperspectral devices based on quasi-random metasurface supercells. The size of the quasi-random metasurface supercell was extended above the wavelength, which enables the exploration of a larger variety of symmetrical supercell structures. Consequently, more quasi-random supercells with lower polarization sensitivity and their spectra with low cross-correlation were obtained. Devices for narrowband spectral reconstruction and broadband hyperspectral single-shot imaging were designed and fabricated. Combined with the genetic algorithm with compressed sensing, the narrowband spectral reconstruction device reconstructs the complex narrowband hyperspectral signal with 6 nm spectral resolution and ultralow errors. The broadband hyperspectral device reconstructs a broadband hyperspectral image (λ/λ ∼ 0.001) with a high average signal fidelity of 92%. This device has the potential to be integrated into a complementary metal-oxide-semiconductor (CMOS) chip for single-shot imaging.

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral trade-off. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from a red-green-blue (RGB) image without complete hyperspectral measurements. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral trade-off, offering simple hardware requirements and potential applications of various machine learning techniques.

Inverse design of photonic crystal filters with arbitrary correlation and size for accurate spectrum reconstruction

Spectroscopic technique based on nanophotonic filters can recover spectral information through compressive sensing theory. The spectral information is encoded by nanophotonic response functions and decoded by computational algorithms. They are generally ultracompact, low in cost, and offer single-shot operation with spectral resolution better than 1 nm. Thus, they could be ideally suited for emerging wearable and portable sensing and imaging applications. Previous work has revealed that successful spectral reconstruction relies on well-designed filter response functions with sufficient randomness and low mutual correlation, but no thorough discussion has been performed on the filter array design. Here, instead of blind selection of filter structures, inverse design algorithms are proposed to obtain a photonic crystal filter array with predefined correlation coefficients and array size. Such rational spectrometer design can perform accurate reconstruction for a complex spectrum and maintain the performance under noise perturbation. We also discuss the impact of correlation coefficient and array size on the spectrum reconstruction accuracy. Our filter design method can be extended to different filter structures and suggests a better encoding component for reconstructive spectrometer applications.

Computational spectrometers enabled by nanophotonics and deep learning

A new type of spectrometer that heavily relies on computational technique to recover spectral information is introduced. They are different from conventional optical spectrometers in many important aspects. Traditional spectrometers offer high spectral resolution and wide spectral range, but they are so bulky and expensive as to be difficult to deploy broadly in the field. Emerging applications in machine sensing and imaging require low-cost miniaturized spectrometers that are specifically designed for certain applications. Computational spectrometers are well suited for these applications. They are generally low in cost and offer single-shot operation, with adequate spectral and spatial resolution. The new type of spectrometer combines recent progress in nanophotonics, advanced signal processing and machine learning. Here we review the recent progress in computational spectrometers, identify key challenges, and note new directions likely to develop in the near future.

Compact fiber-integrated scattering device based on mixed-phase TiO2 for speckle spectrometer

A universal, repeatable, and controllable integration of single-mode optical fiber and mixed-phase TiO₂ is used to manufacture a compact fiber-integrated scattering device. Based on the device, we achieve a high-performance and compact fiber-based speckle spectrometer, which has a resolution of 20 pm over a bandwidth of 15 nm, in the 1550 nm range. We test the capability of our proposed spectrometer to reconstruct narrow linewidth and broadband optical spectrums, and compare the performance with that of a traditional optical spectrum analyzer.

Mass production-enabled computational spectrometers based on multilayer thin films

Multilayer thin film (MTF) filter arrays for computational spectroscopy are fabricated using stencil lithography. The MTF filter array is a 6 × 6 square grid, and 169 identical arrays are fabricated on a single wafer. A computational spectrometer is formed by attaching the MTF filter array on a complementary metal–oxide–semiconductor (CMOS) image sensor. With a single exposure, 36 unique intensities of incident light are collected. The spectrum of the incident light is recovered using collected intensities and numerical optimization techniques. Varied light sources in the wavelength range of 500 to 849 nm are recovered with a spacing of 1 nm. The reconstructed spectra are a good match with the reference spectra, measured by a grating-based spectrometer. We also demonstrate computational pinhole spectral imaging using the MTF filter array. Adapting a spectral scanning method, we collect 36 monochromatic filtered images and reconstructed 350 monochromatic images in the wavelength range of 500 to 849 nm, with a spacing of 1 nm. These computational spectrometers could be useful for various applications that require compact size, high resolution, and wide working range.

Study on the Influence of Porosity of the Nacre Layer on the Luster and Surface Roughness of Chinese Large Freshwater Nucleated Pearl

The Chinese large freshwater nucleated pearl has become popular for its unique appearance throughout the international jewelry market in recent years. However, its quality evaluation mostly depends on appearance observations, and the influence of the nacre layer’s internal microstructure on the gemstone’s appearance needs further investigation. In this study, light reflectivity, surface height unevenness parameters and porosity of the nacre layer were measured by chroma meter, laser scanning confocal microscope and X-ray computed tomography (μ-CT), which quantitatively described the characteristics of luster, surface roughness and structure compactness of the nacre layer. It was found that the porosity of the nacre layer had a significant influence on appearance features, with an increase of porosity showing more surface blemishes (higher surface roughness parameters) and weaker luster (lower reflectivity). Related results can provide reference for the scientific and quantitative evaluation of pearl quality.

Compressive recovery of smartphone RGB spectral sensitivity functions

Spectral response (or sensitivity) functions of a three-color image sensor (or trichromatic camera) allow a mapping from spectral stimuli to RGB color values. Like biological photosensors, digital RGB spectral responses are device dependent and significantly vary from model to model. Thus, the information on the RGB spectral response functions of a specific device is vital in a variety of computer vision as well as mobile health (mHealth) applications. Theoretically, spectral response functions can directly be measured with sophisticated calibration equipment in a specialized laboratory setting, which is not easily accessible for most application developers. As a result, several mathematical methods have been proposed relying on standard color references. Typical optimization frameworks with constraints are often complicated, requiring a large number of colors. We report a compressive sensing framework in the frequency domain for accurately predicting RGB spectral response functions only with several primary colors. Using a scientific camera, we first validate the estimation method with direct spectral sensitivity measurements and ensure that the root mean square errors between the ground truth and recovered RGB spectral response functions are negligible. We further recover the RGB spectral response functions of smartphones and validate with an expanded color checker reference. We expect that this simple yet reliable estimation method of RGB spectral sensitivity can easily be applied for color calibration and standardization in machine vision, hyperspectral filters, and mHealth applications that capitalize on the built-in cameras of smartphones.