Magnetically driven photonic ‘microbots’
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Photonic-crystal surface-emitting lasers
High-performance lasers are important to realize a range of applications including smart mobility and smart manufacturing, for example, through their uses in key technologies such as light detection and ranging (LiDAR) and laser processing. However, existing lasers have a number of performance limitations that hinder their practical use. For example, conventional semiconductor lasers are associated with low brightness and low functionality, even though they are compact and highly efficient. Conventional semiconductor lasers therefore require external optics and mechanical elements for reshaping and scanning of emitted beams, resulting in large, complicated systems for various practical uses. Furthermore, even with such external elements, the brightness of these lasers cannot be sufficiently increased for use in laser processing. Similarly, gas and solid-state lasers, while having high-brightness, are also large and complicated. Photonic-crystal surface-emitting lasers (PCSELs) boast both high brightness and high functionality while maintaining the merits of semiconductor lasers, and thus PCSELs are solutions to the issues of existing laser technologies. In this Review, we discuss recent progress of PCSELs towards high-brightness and high-functionality operations. We then elaborate on new trends such as short-pulse and short-wavelength operations as well as the combination with machine learning and quantum technologies. Finally, we outline future research directions of PCSELs with regard to various applications, including not only LiDAR and laser processing, as described above, but also communications, mobile technologies, and even aerospace and laser fusion.
A hybrid single quantum dot coupled cavity on a CMOS-compatible SiC photonic chip for Purcell-enhanced deterministic single-photon emission
The ability to control nonclassical light emission from a single quantum emitter by an integrated cavity may unleash new perspectives for integrated photonic quantum applications. However, coupling a single quantum emitter to cavity within photonic circuitry towards creation of the Purcell-enhanced single-photon emission is elusive due to the complexity of integrating active devices in low-loss photonic circuits. Here we demonstrate a hybrid micro-ring resonator (HMRR) coupled with self-assembled quantum dots (QDs) for cavity-enhanced deterministic single-photon emission. The HMRR cavity supports whispering-gallery modes with quality factors up to 7.8×103. By further introducing a micro-heater, we show that the photon emission of QDs can be locally and dynamically tuned over one free spectral ranges of the HMRR ( ~ 4 nm). This allows precise tuning of individual QDs in resonance with the cavity modes, thereby enhancing single-photon emission with a Purcell factor of about 4.9. Our results on the hybrid integrated cavities coupled with two-level quantum emitters emerge as promising devices for chip-based scalable photonic quantum applications.
Post-processing methods for delay embedding and feature scaling of reservoir computers
Reservoir computing is a machine learning method that is well-suited for complex time series prediction tasks. Both delay embedding and the projection of input data into a higher-dimensional space play important roles in enabling accurate predictions. We establish simple post-processing methods that train on past node states at uniformly or randomly-delayed timeshifts. These methods improve reservoir computer prediction performance through increased feature dimension and/or better delay embedding. Here we introduce the multi-random-timeshifting method that randomly recalls previous states of reservoir nodes. The use of multi-random-timeshifting allows for smaller reservoirs while maintaining large feature dimensions, is computationally cheap to optimise, and is our preferred post-processing method. For experimentalists, all our post-processing methods can be translated to readout data sampled from physical reservoirs, which we demonstrate using readout data from an experimentally-realised laser reservoir system.
On-fiber photonic nanojet enables super-resolution in en face optical coherence tomography and scattering nanoscopy
Photonic nanojet (PNJ) of subwavelength spot-size originating at shadow side of a microsphere is projected as an imperative optical tool for micro-nano optics applications. Lack of a free hold PNJ source limits its vast potential. A state-of-the-art PNJ is introduced on a chemically etched encaved optical fiber nanoprobe holding a microsphere. Nanoprobe generating optical beam of spot-size ~2 μm, is focused on the microsphere resulting in PNJ of varying spot-size, 0.8λ to 0.5λ, over its’ length ~13λ at 660 nm wavelength. A PNJ centered around 840 nm wavelength combined with an effective numerical aperture ~2 results in an en-face optical coherence tomography (OCT) image of lateral resolution ~247 nm and phase map with optical thickness, ~70 nm for a standard DVD. Further, the PNJ based backscattered nanoscopy of silver nano-particles is exhibited with resolution ~40 nm. This commercially viable PNJ head can be an ideal platform for various microscopies and other applications.
Non-invasive and fully two-dimensional quantitative visualization of transparent flow fields enabled by photonic spin-decoupled metasurfaces
Transparent flow field visualization techniques play a critical role in engineering and scientific applications. They provide a clear and intuitive means to understand fluid dynamics and its complex phenomena, such as laminar flow, turbulence, and vortices. However, achieving fully two-dimensional quantitative visualization of transparent flow fields under non-invasive conditions remains a significant challenge. Here, we present an approach for achieving flow field visualization by harnessing the synergistic effects of a dielectric metasurface array endowed with photonic spin-decoupled capability. This approach enables the simultaneous acquisition of light-field images containing flow field information in two orthogonal dimensions, which allows for the real-time and quantitative derivation of multiple physical parameters. As a proof-of-concept, we experimentally demonstrate the applicability of the proposed visualization technique to various scenarios, including temperature field mapping, gas leak detection, visualization of various fluid physical phenomena, and 3D morphological reconstruction of transparent phase objects. This technique not only establishes an exceptional platform for advancing research in fluid physics, but also exhibits significant potential for broad applications in industrial design and vision.
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