Computational spectrometers enabled by nanophotonics and deep learning

Miniaturized disordered photonic molecule spectrometer

The burgeoning field of computational spectrometers is rapidly advancing, providing a pathway to highly miniaturized, on-chip systems for in-situ or portable measurements. The performance of these systems is typically limited in its encoder section. The response matrix is largely compromised with redundancies, due to the periodic intensity or overly smooth responses. As such, the inherent interdependence among the physical size, resolution, and bandwidth of spectral encoders poses a challenge to further miniaturization progress. Achieving high spectral resolution necessitates a long optical path length, leading to a larger footprint required for sufficient spectral decorrelation, resulting in a limited detectable free-spectral range (FSR). Here, we report a groundbreaking ultra-miniaturized disordered photonic molecule spectrometer that surpasses the resolution-bandwidth-footprint metric of current spectrometers. This computational spectrometer utilizes complicated electromagnetic coupling to determinately generate quasi-random spectral response matrices, a feature absents in other state-of-the-art systems, fundamentally overcoming limitations present in the current technologies. This configuration yields an effectively infinite FSR while upholding a high Q-factor ( > 7.74 × 105). Through dynamic manipulation of photon frequency, amplitude, and phase, a broad operational bandwidth exceeding 100 nm can be attained with an ultra-high spectral resolution of 8 pm, all encapsulated within an ultra-compact footprint measuring 70 × 50 μm². The disordered photonic molecule spectrometer is constructed on a CMOS-compatible integrated photonics platform, presenting a pioneering approach for high-performance and highly manufacturable miniaturized spectroscopy.

A Low-Cost Computational Spectrometer Based on a Trained Sparse Base Matrix

Computational spectrometers based on coded measurement and computational reconstruction have great application prospects. This paper proposes a computational spectrometer that has a low cost, is easy to implement in hardware, and has high reconstruction accuracy. The proposed computational spectrometer uses low-cost but highly correlated polymethyl methacrylate (PMMA) material as broadband encoding filters, which could affect spectral reconstruction accuracy. To alleviate this issue, we decoupled the sensing matrix, which is the product of the measurement matrix and sparse base matrix, and subsequently optimized the sparse base matrix independently. Enlightened by the neural network method, an over-complete dictionary was trained based on the public spectral dataset, which was used as the required sparse base matrix for reconstruction. Through this method, we achieved good reconstruction results in simulation. In experiments, the spectrometer prototype can achieve a high-resolution spectral measurements, demonstrating the feasibility of a low-cost computational spectrometer based on the trained sparse base matrix.

Empowering nanophotonic applications via artificial intelligence: pathways, progress, and prospects

Empowering nanophotonic devices via artificial intelligence (AI) has revolutionized both scientific research methodologies and engineering practices, addressing critical challenges in the design and optimization of complex systems. Traditional methods for developing nanophotonic devices are often constrained by the high dimensionality of design spaces and computational inefficiencies. This review highlights how AI-driven techniques provide transformative solutions by enabling the efficient exploration of vast design spaces, optimizing intricate parameter systems, and predicting the performance of advanced nanophotonic materials and devices with high accuracy. By bridging the gap between computational complexity and practical implementation, AI accelerates the discovery of novel nanophotonic functionalities. Furthermore, we delve into emerging domains, such as diffractive neural networks and quantum machine learning, emphasizing their potential to exploit photonic properties for innovative strategies. The review also examines AI's applications in advanced engineering areas, e.g., optical image recognition, showcasing its role in addressing complex challenges in device integration. By facilitating the development of highly efficient, compact optical devices, these AI-powered methodologies are paving the way for next-generation nanophotonic systems with enhanced functionalities and broader applications.

Highly accurate, efficient, and fabrication tolerance-aware nanostructure prediction for high-performance optoelectronic devices

Despite extensive efforts to predict optimal nanostructures for enhancing optical devices, a more accurate, efficient, and practical method for nanostructure optimisation is required. In particular, fabrication tolerance is a promising avenue for significantly improving manufacturing efficiency; however, research in this area is limited. In this study, we introduce a practical approach for enhancing the performance of optoelectronic devices using an artificial intelligence (AI)-based nanostructure optimisation strategy. We optimised a support vector regression (SVR) model to capture the complex and nonlinear relationships between the transmittance and nanograting structure variables with the goal of improving optoelectronic devices. Our versatile model accurately predicted the continuous transmittance data with high precision (R2 = 0.995) using only 216 training data points. It can also make predictions under untrained conditions, thereby enabling the creation of a transmittance nanostructure contour map (R2 = 0.949). This method facilitates the design of nanostructures tailored to specific optical properties and provides valuable insights into fabrication tolerance. Through experimental validation, we identified an optimal nanograting structure with the highest transmittance in the visible-light spectrum. When integrated into optoelectronic devices such as organic light-emitting diodes (OLEDs) and organic solar cells (OSCs), their performance is significantly improved by increasing the light transmittance. Specifically, devices using the fabricated nanograting film exhibited a 17% improvement in external quantum efficiency (EQE) for solution-processed organic light-emitting diodes (SP-OLEDs) and a 10.7% improvement in power-conversion efficiency (PCE) for OSCs.

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.

Denoising-autoencoder-facilitated MEMS computational spectrometer with enhanced resolution on a silicon photonic chip

Silicon photonics enables the construction of chip-scale spectrometers, in which those using a single tunable interferometer provide a simple and cost-effective solution. Among various tuning mechanisms, electrostatic MEMS reconfiguration stands out as an ideal candidate, given its high tuning efficiency and ultra-low power consumption. Nonetheless, MEMS devices face significant noise challenges arising from their susceptible minuscule components, adversely impacting spectral resolution. Here, we propose a distinct paradigm of spectrometers through synergizing an easily-fabricated MEMS-reconfigurable low-loss waveguide coupler on a silicon photonic chip and a convolutional autoencoder denoising (CAED) mechanism. The spectrometer offers a 300 nm bandwidth and a reconstruction resolution of 0.3 nm in a noise-free condition. In a noisy environment with a signal-to-noise ratio as low as 30 dB, the reconstruction resolution of the interferograms processed by the CAED exhibits an enhancement from 1.2 to 0.4 nm, approaching the noise-free value. Our technology is envisaged to provide a powerful and cost-effective solution for applications requiring accurate, broadband, and energy-efficient spectral analysis.

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.

Very-large-scale-integrated high quality factor nanoantenna pixels

Metasurfaces precisely control the amplitude, polarization and phase of light, with applications spanning imaging, sensing, modulation and computing. Three crucial performance metrics of metasurfaces and their constituent resonators are the quality factor (Q factor), mode volume (Vm) and ability to control far-field radiation. Often, resonators face a trade-off between these parameters: a reduction in Vm leads to an equivalent reduction in Q, albeit with more control over radiation. Here we demonstrate that this perceived compromise is not inevitable: high quality factor, subwavelength Vm and controlled dipole-like radiation can be achieved simultaneously. We design high quality factor, very-large-scale-integrated silicon nanoantenna pixels (VINPix) that combine guided mode resonance waveguides with photonic crystal cavities. With optimized nanoantennas, we achieve Q factors exceeding 1,500 with Vm less than 0.1( λ / n air ) 3 . Each nanoantenna is individually addressable by free-space light and exhibits dipole-like scattering to the far-field. Resonator densities exceeding a million nanoantennas per cm2 can be achieved. As a proof-of-concept application, we show spectrometer-free, spatially localized, refractive-index sensing, and fabrication of an 8 mm × 8 mm VINPix array. Our platform provides a foundation for compact, densely multiplexed devices such as spatial light modulators, computational spectrometers and in situ environmental sensors.

Snapshot computational spectroscopy enabled by deep learning

Spectroscopy is a technique that analyzes the interaction between matter and light as a function of wavelength. It is the most convenient method for obtaining qualitative and quantitative information about an unknown sample with reasonable accuracy. However, traditional spectroscopy is reliant on bulky and expensive spectrometers, while emerging applications of portable, low-cost and lightweight sensing and imaging necessitate the development of miniaturized spectrometers. In this study, we have developed a computational spectroscopy method that can provide single-shot operation, sub-nanometer spectral resolution, and direct materials characterization. This method is enabled by a metasurface integrated computational spectrometer and deep learning algorithms. The identification of critical parameters of optical cavities and chemical solutions is demonstrated through the application of the method, with an average spectral reconstruction accuracy of 0.4 nm and an actual measurement error of 0.32 nm. The mean square errors for the characterization of cavity length and solution concentration are 0.53 % and 1.21 %, respectively. Consequently, computational spectroscopy can achieve the same level of spectral accuracy as traditional spectroscopy while providing convenient, rapid material characterization in a variety of scenarios.

Unique Hyperspectral Response Design Enabled by Periodic Surface Textures in Photodiodes

The applications of hyperspectral imaging across disciplines such as healthcare, automobiles, forensics, and astronomy are constrained by the requirement for intricate filters and dispersion lenses. By utilization of devices with engineered spectral responses and advanced signal processing techniques, the spectral imaging process can be made more approachable across various fields. We propose a spectral response design method employing photon-trapping surface textures (PTSTs), which eliminates the necessity for external diffraction optics and facilitates system miniaturization. We have developed an analytical model to calculate electromagnetic wave coupling using the effective refractive index of silicon in the presence of PTST. We have extensively validated the model against simulations and experimental data, ensuring the accuracy of our predictions. We observe a strong linear relationship between the peak coupling wavelength and the PTST period along with a moderate proportional relation to the PTST diameters. Additionally, we identify a significant correlation between inter-PTST spacing and wave propagation modes. The experimental validation of the model is conducted using PTST-equipped photodiodes fabricated through complementary metal-oxide-semiconductor-compatible processes. Further, we demonstrate the electrical and optical performance of these PTST-equipped photodiodes to show high speed (response time: 27 ps), high gain (multiplication gain, M: 90), and a low operating voltage (breakdown voltage: ∼ 8.0 V). Last, we utilize the distinctive response of the fabricated PTST-equipped photodiode to simulate hyperspectral imaging, providing a proof of principle. These findings are crucial for the progression of on-chip integration of high-performance spectrometers, guaranteeing real-time data manipulation, and cost-effective production of hyperspectral imaging systems.

AI-Based Metamaterial Design

The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.

Rapid in-situ calibration of computational micro-spectrometer with few-shot meta-learning

Computational micro-spectrometers comprised of detector arrays and encoding structure arrays, such as on-chip Fabry-Perot (FP) cavity filters, have great potential in many in-situ applications owing to their compact size and snapshot imaging ability. Given manufacturing deviation and environmental influence are inevitable, easy and effective calibration for spectrometer is necessary, especially for in-situ applications. Currently calibration strategies based on iterative algorithms or neural networks require accurate measurements of pixel-level (spectral) encoding functions through monochromator or large amounts of standard samples. These procedures are time-consuming and expensive, thereby impeding in-situ applications. Meta-learning algorithms with few-shot learning ability can address this challenge by incorporating the prior knowledge in the simulated dataset. In this work, we propose a meta-learning algorithm free of measuring encoding function or large amounts of standard samples to calibrate a micro-spectrometer with manufacturing deviation effectively. Our micro-spectrometer comprises 16 types of FP filters covering a wavelength range of 550-720 nm. The center wavelength of each filter type deviates from the design up to 6 nm. After calibration with 15 different color data, the average reconstruction error on the test dataset decreased from 7.2 × 10-3 to 1.2 × 10-3, and further decreased to 9.4 × 10-4 when the calibration data increased to 24. The performance is comparable to algorithms trained with measured encoding function both in reconstruction error and generalization ability. We estimated that the cost of in-situ calibration through reflectance measurements of color chart decreased to one percent of the cost through monochromator measurements. By exploiting prior deviation information in simulation data with meta-learning, the efficiency and cost of calibration are significantly improved, thereby facilitating the large-scale production and in-situ application of micro-spectrometers.

Nonlinear self-calibrated spectrometer with single GeSe-InSe heterojunction device

Computational spectrometry is an emerging field that uses photodetection in conjunction with numerical algorithms for spectroscopic measurements. Compact single photodetectors made from layered materials are particularly attractive since they eliminate the need for bulky mechanical and optical components used in traditional spectrometers and can easily be engineered as heterostructures to optimize device performance. However, such photodetectors are typically nonlinear devices, which adds complexity to extracting optical spectra from their response. Here, we train an artificial neural network to recover the full nonlinear spectral photoresponse of a single GeSe-InSe p-n heterojunction device. The device has a spectral range of 400 to 1100 nm, a small footprint of ~25 × 25 square micrometers, and a mean reconstruction error of 2 × 10-4 for the power spectrum at 0.35 nanometers. Using our device, we demonstrate a solution to metamerism, an apparent matching of colors with different power spectral distributions, which is a fundamental problem in optical imaging.

Pixelated gradient thickness optical filter for visible light spectroscopy

A miniature low-cost pixelated gradient thickness optical filter is proposed to achieve spectroscopy in the visible wavelength range. The optical filter consists of a two-dimensional array of metal-dielectric-metal thin films arranged in Fabry-Pérot filter configurations with discretely varying cavity thicknesses. The wavelength-selective characterization of each filter is performed by measuring the transmittance over the visible wavelength range. The pixelated gradient thickness filter is equipped with a CMOS image sensor, and its performance as a spectroscopic module is evaluated by illuminating different monochromatic wavelengths on it. The target spectra are successfully reconstructed from the output signals recorded in the sensor from the respective pixelated gradient thickness filters. The technological competence of the proposed filter will enable its use in handheld devices to widen its application range in day-to-day life.

Gradient-metasurface directional photodetectors

Angle-sensitive photodetectors are a promising device technology for many advanced imaging functionalities, including lensless compound-eye vision, lightfield sensing, optical spatial filtering, and phase imaging. Here we demonstrate the use of plasmonic gradient metasurfaces to tailor the angular response of generic planar photodetectors. The resulting devices rely on the phase-matched coupling of light incident at select geometrically tunable angles into guided plasmonic modes, which are then scattered and absorbed in the underlying photodetector active layer. This approach naturally introduces sharp peaks in the angular response, with smaller footprint and reduced guided-mode radiative losses (and therefore improved spatial resolution and sensitivity) compared to analogous devices based on diffractive coupling. More broadly, these results highlight a promising new application space of flat optics, where gradient metasurfaces are integrated within image sensors to enable unconventional capabilities with enhanced system miniaturization and design flexibility.

A Neural Network Computational Spectrometer Trained by a Small Dataset with High-Correlation Optical Filters

A computational spectrometer is a novel form of spectrometer powerful for portable in situ applications. In the encoding part of the computational spectrometer, filters with highly non-correlated properties are requisite for compressed sensing, which poses severe challenges for optical design and fabrication. In the reconstruction part of the computational spectrometer, conventional iterative reconstruction algorithms are featured with limited efficiency and accuracy, which hinders their application for real-time in situ measurements. This study proposes a neural network computational spectrometer trained by a small dataset with high-correlation optical filters. We aim to change the paradigm by which the accuracy of neural network computational spectrometers depends heavily on the amount of training data and the non-correlation property of optical filters. First, we propose a presumption about a distribution law for the common large training dataset, in which a unique widespread distribution law is shown when calculating the spectrum correlation. Based on that, we extract the original dataset according to the distribution probability and form a small training dataset. Then a fully connected neural network architecture is constructed to perform the reconstruction. After that, a group of thin film filters are introduced to work as the encoding layer. Then the neural network is trained by a small dataset under high-correlation filters and applied in simulation. Finally, the experiment is carried out and the result indicates that the neural network enabled by a small training dataset has performed very well with the thin film filters. This study may provide a reference for computational spectrometers based on high-correlation optical filters.

Compact angle-resolved metasurface spectrometer

Light scattered or radiated from a material carries valuable information on the said material. Such information can be uncovered by measuring the light field at different angles and frequencies. However, this technique typically requires a large optical apparatus, hampering the widespread use of angle-resolved spectroscopy beyond the lab. Here we demonstrate compact angle-resolved spectral imaging by combining a tunable metasurface-based spectrometer array and a metalens. With this approach, even with a miniaturized spectrometer footprint of only 4 × 4 μm2, we demonstrate a wavelength accuracy of 0.17 nm, spectral resolution of 0.4 nm and a linear dynamic range of 149 dB. Moreover, our spectrometer has a detection limit of 1.2 fJ, and can be patterned to an array for spectral imaging. Placing such a spectrometer array directly at the back focal plane of a metalens, we achieve an angular resolution of 4.88 × 10-3 rad. Our angle-resolved spectrometers empowered by metalenses can be employed towards enhancing advanced optical imaging and spectral analysis applications.

High-sensitivity computational miniaturized terahertz spectrometer using a plasmonic filter array and a modified multilayer residual CNN

Spectrometer miniaturization is desired for handheld and portable applications, yet nearly no miniaturized spectrometer is reported operating within terahertz (THz) waveband. Computational strategy, which can acquire incident spectral information through encoding and decoding it using optical devices and reconstruction algorithms, respectively, is widely employed in spectrometer miniaturization as artificial intelligence emerges. We demonstrate a computational miniaturized THz spectrometer, where a plasmonic filter array tailors the spectral response of a blocked-impurity-band detector. Besides, an adaptive deep-learning algorithm is proposed for spectral reconstructions with curbing the negative impact from the optical property of the filter array. Our spectrometer achieves modest spectral resolution (2.3 cm-1) compared with visible and infrared miniaturized spectrometers, outstanding sensitivity (e.g., signal-to-noise ratio, 6.4E6: 1) superior to common benchtop THz spectrometers. The combination of THz optical devices and reconstruction algorithms provides a route toward THz spectrometer miniaturization, and further extends the applicable sphere of the THz spectroscopy technique.

Miniature integrated spectrometers towards high-performance and cost-effective

The conjugated mode of bound states in a continuum is integrated as a narrowband wavelength extraction unit. A low-cost and easy-to-prepare strategy, using solution-processable semiconductors, has been demonstrated to form a new platform for on-chip spectral analysis.

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.

Cost-Effective Nanophotonic Metasurfaces with Spatially Gradient Structures for Ultrasensitive Imaging-Based Refractometric Sensing

Nanophotonic metasurfaces are widely utilized in various domains, such as biomedical, healthcare, and environmental monitoring, benefiting from their unique advantages of label-free, noninvasive, and real-time response. However, nanophotonic metasurfaces usually rely on sophisticated instruments, and expensive and time-consuming fabrication processes, which severely restricts their practical applications. Herein, a spatially gradient metasurface is integrated with an imaging-based sensing scheme, waiving the requirement of spectrometers and achieving an ultrahigh imaging-based sensitivity of 3321 pixels/refractive index unit superior to that characterized using conventional compact spectrometers. The metasurface is fabricated by nanoimprint lithography using a reusable cyclic olefin copolymer template featuring millions of unique nanostructures. Under the illumination of monochromatic light, the transmittance of different nanostructures on the metasurface differs, resulting in grayscale images with varied intensity distributions. Analyzing the intensity change of the metasurface's recorded image can obtain the covering medium's refractive index. Furthermore, through theory and experimentation, the high reliability of the proposed reusable and flexible template has been verified for nanophotonic metasurface fabrication which further reduces the fabrication cost of core sensing elements. Finally, with proper optimization of the metasurface structure and imaging system, this setup is expected to be applied to many emerging areas of point-of-care, real-time, and on-site biosensing.

InSPECtor: an end-to-end design framework for compressive pixelated hyperspectral instruments

Classic designs of hyperspectral instrumentation densely sample the spatial and spectral information of the scene of interest. Data may be compressed after the acquisition. In this paper, we introduce a framework for the design of an optimized, micropatterned snapshot hyperspectral imager that acquires an optimized subset of the spatial and spectral information in the scene. The data is thereby already compressed at the sensor level but can be restored to the full hyperspectral data cube by the jointly optimized reconstructor. This framework is implemented with TensorFlow and makes use of its automatic differentiation for the joint optimization of the layout of the micropatterned filter array as well as the reconstructor. We explore the achievable compression ratio for different numbers of filter passbands, number of scanning frames, and filter layouts using data collected by the Hyperscout instrument. We show resulting instrument designs that take snapshot measurements without losing significant information while reducing the data volume, acquisition time, or detector space by a factor of 40 as compared to classic, dense sampling. The joint optimization of a compressive hyperspectral imager design and the accompanying reconstructor provides an avenue to substantially reduce the data volume from hyperspectral imagers.

Compact wavemeter incorporating femtosecond laser-induced surface nanostructures enabled by deep learning

Miniature spectrometers have the advantage of high portability and integration, making them quick and easy to use in various working environments. The speckle patterns produced by light scattering through a disordered medium are highly sensitive to wavelength changes and can be used to design high-precision wavemeters and spectrometers. In this study, we used a self-organized, femtosecond laser-prepared nanostructure with a characteristic size of approximately 30-50 nm on a sapphire surface as a scattering medium to effectively induce spectral dispersion. By leveraging this random scattering structure, we successfully designed a compact scattering wavelength meter with efficient scattering properties. The collected speckle patterns were identified and classified using a neural network, and the variation of speckle patterns with wavelength was accurately extracted, achieving a measurement accuracy of 10 pm in multiple wavelength ranges. The system can effectively suppress instrument and environmental noise with high robustness. This work paves the way for the development of compact high-precision wavemeters.

Quantum-dot light-chip micro-spectrometer

Micro-spectrometers have great potential in various fields such as medicine, agriculture, and aerospace. In this work, a quantum-dot (QD) light-chip micro-spectrometer is proposed in which QDs emit different wavelengths of light that are combined with a spectral reconstruction (SR) algorithm. The QD array itself can play the roles of both the light source and the wavelength division structure. The spectra of samples can be obtained by using this simple light source with a detector and algorithm, and the spectral resolution reaches 9.7 nm in the wavelength range from 580 nm to 720 nm. The area of the QD light chip is 4 × 7.5 mm², which is 20 times smaller than the halogen light sources of commercial spectrometers. It does not need a wavelength division structure and greatly reduces the volume of the spectrometer. Such a micro-spectrometer can be used for material identification: in a demonstration, three kinds of transparent samples, real and fake leaves, and real and fake blood were classified with an accuracy of 100%. These results indicate that the spectrometer based on a QD light chip has broad application prospects.

Geometric deep optical sensing

Geometry, an ancient yet vibrant branch of mathematics, has important and far-reaching impacts on various disciplines such as art, science, and engineering. Here, we introduce an emerging concept dubbed "geometric deep optical sensing" that is based on a number of recent demonstrations in advanced optical sensing and imaging, in which a reconfigurable sensor (or an array thereof) can directly decipher the rich information of an unknown incident light beam, including its intensity, spectrum, polarization, spatial features, and possibly angular momentum. We present the physical, mathematical, and engineering foundations of this concept, with particular emphases on the roles of classical and quantum geometry and deep neural networks. Furthermore, we discuss the new opportunities that this emerging scheme can enable and the challenges associated with future developments.

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.