Nanophotonic array-induced dynamic behavior for label-free shape-selective bacteria sieving.

Single chip integrated spectrometers are critical to bring chemical and biological sensing, spectroscopy, and spectral imaging into robust, compact and cost-effective devices. Existing on-chip spectrometer approaches fail to realize both high resolution and broad band. Here we demonstrate a microring resonator-assisted Fourier-transform (RAFT) spectrometer, which is realized using a tunable Mach-Zehnder interferometer (MZI) cascaded with a tunable microring resonator (MRR) to enhance the resolution, integrated with a photodetector onto a single chip. The MRR boosts the resolution to 0.47 nm, far beyond the Rayleigh criterion of the tunable MZI-based Fourier-transform spectrometer. A single channel achieves large bandwidth of ~ 90 nm with low power consumption (35 mW for MRR and 1.8 W for MZI) at the expense of degraded signal-to-noise ratio due to time-multiplexing. Integrating a RAFT element array is envisaged to dramatically extend the bandwidth for spectral analytical applications such as chemical and biological sensing, spectroscopy, image spectrometry, etc.

Recent advances in silicon photonic chips have made huge progress in optical computing owing to their flexibility in the reconfiguration of various tasks. Its deployment of neural networks serves as an alternative for mitigating the rapidly increased demand for computing resources in electronic platforms. However, it remains a formidable challenge to train the online programmable optical neural networks efficiently, being restricted by the difficulty in obtaining gradient information on a physical device when executing a gradient descent algorithm. Here, we experimentally demonstrate an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip. A gradient-free algorithm, that is, the genetic algorithm, is adopted. The protocol is on-chip implementable, physical agnostic (no need to rely on characterization and offline modeling), and gradientfree. The protocol works for various types of chip structures and is especially helpful to those that cannot be analytically decomposed and characterized. We confirm its viability using several practical tasks, including the crossbar switch and the Iris classification. Finally, by comparing our physics-agonistic and gradient-free method to the off-chip and gradient-based training methods, we demonstrate the robustness of our system to perturbations such as imperfect phase implementation and photodetection noise. Optical processors with gradient-free genetic algorithms have broad application potentials in pattern recognition, reinforcement learning, quantum computing, and realistic applications (such as facial recognition, natural language processing, and autonomous vehicles).

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Spatial manipulation of a precise number of viruses for host cell infection is essential for the extensive studies of virus pathogenesis and evolution. Albeit optical tweezers have been advanced to the atomic level via optical cooling, it is still challenging to efficiently trap and manipulate arbitrary number of viruses in an aqueous environment, being restricted by insufficient strength of optical forces and a lack of multifunctional spatial manipulation techniques. Here, by employing the virus hopping and flexibility of moving the laser position, multifunctional virus manipulation with a large trapping area is demonstrated, enabling single or massive (a large quantity of) virus transporting, positioning, patterning, sorting, and concentrating. The enhanced optical forces are produced by the confinement of light in engineered arrays of nanocavities by fine tuning of the interference resonances, and this approach allows trapping and moving viruses down to 40 nm in size. The work paves the way to efficient and precise manipulation of either single or massive groups of viruses, opening a wide range of novel opportunities for virus pathogenesis and inhibitor development at the single‐virus level.