Learned optics, which incorporate lightweight diffractive optics, coded-aperture modulation, and specialized image-processing neural networks, have recently garnered attention in the field of snapshot hyperspectral imaging (HSI). While conventional methods typically rely on a single lens element paired with an off-the-shelf color sensor, these setups, despite their widespread availability, present inherent limitations. First, the Bayer sensor's spectral response curves are not optimized for HSI applications, limiting spectral fidelity of the reconstruction. Second, single lens designs rely on a single diffractive optical element (DOE) to simultaneously encode spectral information and maintain spatial resolution across all wavelengths, which constrains spectral encoding capabilities. This work investigates a multi-channel lens array combined with aperture-wise color filters, all co-optimized alongside an image reconstruction network. This configuration enables independent spatial encoding and spectral response for each channel, improving optical encoding across both spatial and spectral dimensions. Specifically, we validate that the method achieves over a 5dB improvement in PSNR for spectral reconstruction compared to existing single-diffractive lens and coded-aperture techniques. Experimental validation further confirmed that the method is capable of recovering up to 31 spectral bands within the 429--700 nm range in diverse indoor and outdoor environments.

Nanoscale measurement is an essential task of nanomanufacturing, and measurement traceability is a fundamental aspect of nanoscale measurement. High-precision nanoscale measurement instruments (e.g. atomic force microscopes (AFM) and scanning electron microscopes (SEM)) need to be calibrated by traceable standards to ensure their accuracy and reliability. However, due to the suboptimal accuracy, uniformity, and consistency of existing standards, they need to be calibrated by metrological instruments traceable to primary length standards (e.g. physical wavelength standards) before use. This results in a long traceability chain that leads to error accumulation and significantly reduces calibration efficiency. This paper proposes a novel shortened and simplified traceability chain, where the physical wavelength standard corresponding to the ⁷S₃ → ⁷P₄° transition frequency of chromium atoms is materialized into self-traceable gratings using the atom lithography technology. The self-traceable gratings can then be directly applied for calibrating measurement instruments. To verify this approach, the self-traceable gratings are calibrated using a metrological AFM of the Physikalisch-Technische Bundesanstalt. Measurement results confirmed the feasibility of the approach. Particularly, our results show that the self-traceable gratings have excellent uniformity over different measurement areas and consistency over different samples, both at 0.001 nm level. Finally, the application of the self-traceable gratings for the calibrations of a commercial AFM and SEM is demonstrated. The new traceability chain significantly simplifies the calibration process, providing a more reliable and higher efficient calibration approach for advanced nanomanufacturing than that of the state-of-the-art.

Depth imaging is very important for many emerging technologies, such as artificial intelligence, driverless vehicles and facial recognition. However, all these applications demand compact and low-power systems that are beyond the capabilities of most state-of-art depth cameras. Recently, metasurface-based depth imaging that exploits point spread function (PSF) engineering has been demonstrated to be miniaturized and single shot without requiring active illumination or multiple viewpoint exposures. A pair of spatially adjacent metalenses with an extended depth-of-field (EDOF) PSF and a depth-sensitive double-helix PSF (DH-PSF) were used, using the former metalens to reconstruct clear images of each depth and the latter to accurately estimate depth. However, due to these two metalenses being non-coaxial, parallax in capturing scenes is inevitable, which would limit the depth precision and field of view. In this work, a bifunctional reconfigurable metalens for 3D depth imaging was proposed by dynamically switching between EDOF-PSF and DH-PSF. Specifically, a polarization-independent metalens working at 1550 nm with a compact 1 mm² aperture was realized, which can generate a focused accelerating beam and a focused rotating beam at the phase transition of crystalline and amorphous Ge₂Sb₂Te₅ (GST), respectively. Combined with the deconvolution algorithm, we demonstrated the good capabilities of scene reconstruction and depth imaging using a theoretical simulation and achieved a depth measurement error of only 3.42%.

In recent years, attention has been directed towards cost-effective and compact freeform Schwarzschild imaging spectrometers with plane gratings. The utilization of tolerance analysis serves as a potent approach to facilitate the development of prototypes. Conventional tolerance analysis methods often rely solely on the modulation transfer function (MTF) criterion. However, for a spectrometer system, factors such as the keystone/smile distortion and spectral resolution performance also require consideration. In this study, a tailored comprehensive performance domain tolerance analysis methodology for freeform imaging spectrometers was developed, considering vital aspects such as the MTF, keystone/smile distortion, and spectral resolution. Through this approach, meticulous tolerance analysis was conducted for a freeform Schwarzschild imaging spectrometer, providing valuable insights for the prototype machining and assembly processes. Emphasis was placed on the necessity of precise control over the tilt and decenter between the first and third mirrors, whereas the other fabrication and assembly tolerances adhered to the standard requirements. Finally, an alignment computer-generated hologram (CGH) was employed for the preassembly of the first and third mirrors, enabling successful prototype development. The congruence observed between the measured results and tolerance analysis outcomes demonstrates the effectiveness of the proposed method.

Species discrimination of insects is an important aspect of ecology and biodiversity research. The traditional methods based on human visual experience and biochemical analysis cannot strike a balance between accuracy and timeliness. Morphological identification using computer vision and machine learning is expected to solve this problem, but image features have poor accuracy for very similar species and usually require complicated networks that are unfriendly to portable edge devices. In this work, we propose a fast and accurate species discrimination method of similar insects using hyperspectral features and lightweight machine learning algorithm. Feature regions selection, feature spectra selection and model quantification are used for the optimization of discriminating network. The experimental results of six similar butterfly species in the genus of Graphium show that, compared with morphological recognition with machine vision, our work achieves a higher accuracy of 92.36 ± 3.04% and a shorter inference time of 0.6 ms, with the tiny-size convolutional neural network deployed on a neural network chip. This study provides a rapid and high-accuracy species discrimination method for insects with high appearance similarity and paves the way for field discriminations using intelligent micro-spectrometer based on on-chip microstructure and artificial intelligence chip.