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.

General-purpose mid-infrared micro-spectrometer based on hierarchical residual CNN and data augmentation.

Taking advantage of broad response range and snap-shot operation mode, reconstructive spectrometers based on integrated frequency-modulation microstructure and computational techniques attract lots of attention. The key problems in reconstruction are sparse samplings related with the limited detectors and generalization ability due to data-driving principle. Here, we demonstrate abstractly a mid-infrared micro-spectrometer covering 2.5-5 µm, which utilizes a grating-integrated lead selenide detector array for sampling and a hierarchal residual convolutional neural network (HRCNN) for reconstructions. Leveraging data augmentation and the powerful feature extraction ability of HRCNN, a spectral resolution of 15 nm is realized. Over one hundred chemicals, including untrained chemicals species tested with an average reconstruction error of ∼1E-4, exhibit the excellent reliability of the micro-spectrometer. The demonstration of the micro-spectrometer promotes the development of the reconstructed strategy.

Recognition of Abnormal-Laying Hens Based on Fast Continuous Wavelet and Deep Learning Using Hyperspectral Images.

The egg production of laying hens is crucial to breeding enterprises in the laying hen breeding industry. However, there is currently no systematic or accurate method to identify low-egg-production-laying hens in commercial farms, and the majority of these hens are identified by breeders based on their experience. In order to address this issue, we propose a method that is widely applicable and highly precise. First, breeders themselves separate low-egg-production-laying hens and normal-laying hens. Then, under a halogen lamp, hyperspectral images of the two different types of hens are captured via hyperspectral imaging equipment. The vertex component analysis (VCA) algorithm is used to extract the cockscomb end member spectrum to obtain the cockscomb spectral feature curves of low-egg-production-laying hens and normal ones. Next, fast continuous wavelet transform (FCWT) is employed to analyze the data of the feature curves in order to obtain the two-dimensional spectral feature image dataset. Finally, referring to the two-dimensional spectral image dataset of the low-egg-production-laying hens and normal ones, we developed a deep learning model based on a convolutional neural network (CNN). When we tested the model's accuracy by using the prepared dataset, we found that it was 0.975 percent accurate. This outcome demonstrates our identification method, which combines hyperspectral imaging technology, an FCWT data analysis method, and a CNN deep learning model, and is highly effective and precise in laying-hen breeding plants. Furthermore, the attempt to use FCWT for the analysis and processing of hyperspectral data will have a significant impact on the research and application of hyperspectral technology in other fields due to its high efficiency and resolution characteristics for data signal analysis and processing.

Highly Efficient and Stable CdZnSeS/ZnSeS Quantum Dots for Application in White Light-Emitting Diode.

Semiconductor quantum dots (QDs) are a promising luminescent phosphor for next-generation lightings and displays. In particular, QD-based white light-emitting diodes (WLEDs) are considered to be the candidate light sources with the most potential for application in displays. In this work, we synthesized quaternary/ternary core/shell alloyed CdZnSeS/ZnSeS QDs with high bright emission intensity. The QDs show good thermal stability by performing high temperature-dependent experiments that range from 295 to 433 K. Finally, the WLED based on the CdZnSeS/ZnSeS QDs exhibits a luminous efficiency (LE) of 28.14 lm/W, an external quantum efficiency (EQE) of 14.86%, and a warm bright sunlight close to the spectrum of daylight (Commission Internationale de l'éclairage (CIE) coordinates 0.305, 0.371). Moreover, the photoluminescence (PL) intensity, LE, EQE, and correlated color temperature (CCT) of as-prepared QD WLED remained relatively stable with only slight changes in the luminescence stability experiment.

Beam drift error and control technology for scanning beam interference lithography.

To improve the quality of grating masks made by scanning beam interference lithography, this article established a mathematical model of step-scanning exposure and analyzed the effects of the beam drift error on the interference image. Beam angle drift can be decomposed into the drift error δx in the exposure plane (XOZ plane) and the drift error δy perpendicular to the plane. Analysis shows that the δx has a major impact on the interference fringes during exposure, which may affect the precision of phase lock. δy leads to the appearance of deflected interference strips and affects the exposure dose. When a low-frequency drift error appears in the light path, the exposure contrast on the photoresist will decrease with the exposure process, which makes the fabrication of large-size diffraction gratings difficult. Furthermore, taking advantage of the characteristics of a scanning beam interference lithography system, an exposed beam stable system was designed that can effectively suppress the low-frequency drift of the beam. The total beam angle control accuracy is better than the 2.7 µrad, and position control accuracy is better than 3.9 µm (both for 1σ), which achieves the expected goal of the design.

[Full-field and automatic methodology of spectral calibration for PGP imaging spectrometer].

In order to analyze spectral data quantitatively which is obtained by prism-grating-prism imaging spectrometer, spectral calibration is required in order to determine spectral characteristics of PGP imaging spectrometer, such as the center wavelength of every spectral channel, spectral resolution and spectral bending. A spectral calibration system of full field based on collimated monochromatic light method is designed. Spherical mirror is used to provide collimated light, and a freely sliding and rotating folding mirror is adopted to change the angle of incident light in order to realize full field and automatic calibration of imaging spectrometer. Experiments of spectral calibration have been done for PGP imaging spectrometer to obtain parameters of spectral performance, and accuracy analysis combined with the structural features of the entire spectral calibration system have been done. Analysis results indicate that spectral calibration accuracy of the calibration system reaches 0.1 nm, and the bandwidth accuracy reaches 1.3%. The calibration system has merits of small size, better commonality, high precision and so on, and because of adopting the control of automation, the additional errors which are caused by human are avoided. The calibration system can be used for spectral calibration of other imaging spectrometers whose structures are similar to PGP.

[Research on influencing factors and compensation method of testing results for concave gratings diffraction efficiency].

The present paper aimed at the question that the beam cross-section changes according to the rotation of concave grating during the measuring process, and the appropriate supplement to the principle of the program was done. The appropriate supplement to the principle lay the foundation for the derivation of the beam cross-section k(theta), and the whole analytical expression of changes factor of the beam cross-section k (theta) for concave grating was derived. Because of the relationship between the theoretical values and the factors which affect the diffraction efficiency measuring accuracy was nonlinear, the quadratic nonlinear regression analysis was introduced and the compensating formula was established. The results show further correction to diffraction efficiency measurements for concave grating, the range of differences between the compensated values and the theoretical values was reduced from +/- 2.5% to less than +/- 0.3%, compared with the linear regression analysis, and the quadratic nonlinear regression analysis significantly reduces the variation between the compensated values and the theoretical values, which further ensures the accurate measurement of the diffraction efficiency for concave gratings. The compensating process is embedded in the measurement program; this method is strongly real-time, which can satisfy the requirements of simple operation, testing speediness and preciseness.

[The integrative design for imaging spectrometer].

The hyperspectrum imaging spectrometer will achieve miniaturization and high spectrum resolution and high space resolution along with development of the hyperspectrum imaging technology that is becoming a trend. This trend requires the designers to improve and optimize their designing constantly in designing the instruments. The present paper carried out a method of integrative design for imaging spectrometer. This method suggested that the design and optimization work of the disperse systems of imaging spectrometers would take into account the whole systems, but not consider themselves only. It would get a perfect result by using this method. This paper also explained in details how the method can be used in the design course of imaging spectrometer with convex grating which has been used widely recently. Finally, this paper validated the method by testing the imaging spectrometer with convex grating, which was developed using this method.