Vortex beams accompanied with orbital angular momentum have attracted significant attention in research fields due to their formidable capabilities in various crucial applications. However, conventional devices for generating vortex beams still suffer from bulky sizes, high cost, and confined performances. Metalens, as an advanced platform to arbitrarily control the optical waves, has promising prospects to address the predicament for conventional devices. Although great progress has been demonstrated in the applications of vortex beams, they are still confronted with fixed functionality after fabrication that severely hinders their application range. In this work, the phase-change material of Ge₂Sb₂Te₅ (GST) is employed to design the meta-atoms to realize tunable optical responses. Moreover, the focused vortex beam can be accomplished by superimposing a helical phase and hyperbolic phase, and the chromatic aberrations in near-infrared (NIR) range can be corrected by introducing an additional phase compensation. And the design strategy is validated by two different metalenses (BAMTF-1 and BAMTF-2). The numerical results indicate that the chromatic aberrations for two metalens can be corrected in 1.33–1.60 μm covering the telecom range. Moreover, the average focusing efficiency of BAMTF-1 is 51.4%, and that of BAMTF-2 is 39.9%, indicating the favorable performances of designed BAMTF. More importantly, their average focal lengths have a relative tuning range of 38.82% and 33.17% by altering the crystallization ratio of GST, respectively. This work may provide a significant scheme for on-chip and tunable devices for NIR imaging and communication systems.

Non-invasive near-infrared (NIR) human glucose sensors have attracted great interest in managing diabetes mellitus and those with complex sensing backgrounds due to glucose absorption spectrum overlap. Here, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using a multilevel metric learning-back propagation network, i.e., “HMML-BP”, based on the narrowband multi-wavelength light NIR system. Our human glucose sensing method combines the advantages of this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to predict blood glucose concentrations. The narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm and a spectral resolution below 12 nm. The six NIR channels measured were first entered into the MML modules to build 3D multi-wavelength light data. Next, 3D multi-wavelength light data were optimized by stochastic neighbor embedding. Diffusion maps and factor analysis algorithms were used to retain effective NIR information. Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. Our blood glucose sensing method provides a new way of utilizing multi-wavelength light and hyperspectral information for smart human glucose monitoring.

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network “HSC3D” for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.