Shaping the light beam is always essential for laser technology and its applications. Among the shaping technologies, shaping the laser in its Fourier domain is a widely used and effective method, such as a pulse shaper, or a 4f system with a phase mask or an iris in between. Orbital angular momentum (OAM) modes spectrum, the Fourier transform of the light field in azimuth, provides a perspective for shaping the light. Here, we propose and experimentally demonstrate a shaping strategy for the azimuthal field by modulating the complex amplitude of the OAM mode spectrum. The scheme utilizes multi-plane light conversion technology and consists only of a spatial light modulator and a mirror. Multiple functions, including beam rotating, beam splitting/combining in azimuth, and OAM mode filtering, are demonstrated. Our work provides a compact and programmable solution for modulating the OAM mode spectrum and shaping beams in azimuth.

Recently, deep learning has been used to establish the nonlinear and nonintuitive mapping between physical structures and electromagnetic responses of meta-atoms for higher computational efficiency. However, to obtain sufficiently accurate predictions, the conventional deep-learning-based method consumes excessive time to collect the data set, thus hindering its wide application in this interdisciplinary field. We introduce a spectral transfer-learning-based metasurface design method to achieve excellent performance on a small data set with only 1000 samples in the target waveband by utilizing open-source data from another spectral range. We demonstrate three transfer strategies and experimentally quantify their performance, among which the “frozen-none” robustly improves the prediction accuracy by ∼26% compared to direct learning. We propose to use a complex-valued deep neural network during the training process to further improve the spectral predicting precision by ∼30% compared to its real-valued counterparts. We design several typical teraherz metadevices by employing a hybrid inverse model consolidating this trained target network and a global optimization algorithm. The simulated results successfully validate the capability of our approach. Our work provides a universal methodology for efficient and accurate metasurface design in arbitrary wavebands, which will pave the way toward the automated and mass production of metasurfaces.

A pin-like beam is a kind of structured light with a special intensity distribution that can be against diffraction, which can be seen as a kind of quasi-nondiffracting beam (Q-NDB). Due to its wide applications, recently, numerous researchers have used optical lenses or on-chip integrated optical diffractive elements to generate this kind of beam. We theoretically verify and experimentally demonstrate an all-fiber solution to generate a subwavelength inverted pin beam by integrating a simple plasma structure on the fiber end surface. The output beams generated by two kinds of plasma structures, i.e., nanoring slot and nanopetal structure, are investigated and measured experimentally. The results show that both the structures are capable of generating subwavelength beams, and the beam generated using the nanopetal structure has the sidelobe suppression ability along the x-axis direction. Our all-fiber device can be flexibly inserted into liquid environments such as cell cultures, blood, and biological tissue fluids to illuminate or stimulate biological cells and molecules in them. It provides a promising fiber-integrated solution for exploring light–matter interaction with subwavelength resolution in the field of biological research.

Although visible femtosecond lasers based on nonlinear frequency conversion of Ti:sapphire femtosecond oscillators or near-infrared ultrafast lasers have been well developed, limitations in terms of footprint, cost, and efficiency have called for alternative laser solutions. The fiber femtosecond mode-locked oscillator as an ideal solution has achieved great success in the 0.9 to 3.5 μm infrared wavelengths, but remains an outstanding challenge in the visible spectrum (390 to 780 nm). Here, we tackle this challenge by introducing a visible-wavelength mode-locked femtosecond fiber oscillator along with an amplifier. This fiber femtosecond oscillator emits red light at 635 nm, employs a figure-nine cavity configuration, applies a double-clad Pr3+-doped fluoride fiber as the visible gain medium, incorporates a visible-wavelength phase-biased nonlinear amplifying loop mirror (PB-NALM) for mode locking, and utilizes a pair of customized high-efficiency and high-groove-density diffraction gratings for dispersion management. Visible self-starting mode locking established by the PB-NALM directly yields red laser pulses with a minimum pulse duration of 196 fs and a repetition rate of 53.957 MHz from the oscillator. Precise control of the grating pair spacing can switch the pulse state from a dissipative soliton or a stretched-pulse soliton to a conventional soliton. In addition, a chirped-pulse amplification system built alongside the oscillator immensely boosts the laser performance, resulting in an average output power over 1 W, a pulse energy of 19.55 nJ, and a dechirped pulse duration of 230 fs. Our result represents a concrete step toward high-power femtosecond fiber lasers covering the visible spectral region and could have important applications in industrial processing, biomedicine, and scientific research.

Optical metasurfaces, which consist of subwavelength scale meta-atoms, represent a novel platform to manipulate the polarization and phase of light. The optical performance of metasurfaces heavily relies on the quality of nanofabrication. Retrieving the Jones matrix of an imperfect metasurface optical element is highly desirable. We show that this can be realized by decomposing the generalized Jones matrix of a meta-atom into two parallel ones, which correspond to the ideal matrix and a phase retardation. To experimentally verify this concept, we designed and fabricated metasurface polarizers, which consist of geometric phase-controlled dielectric meta-atoms. By scanning the polarization states of the incident and transmitted light, we are able to extract the coefficients of the two parallel matrices of a metasurface polarizer. Based on the results of the Jones matrix decomposition, we also demonstrated polarization image encryption and spin-selective optical holography. The proposed Jones matrix retrieval protocol may have important applications in computational imaging, optical computing, optical communications, and so on.

Chiral sum-frequency generation (SFG) has proven to be a versatile spectroscopic and imaging tool for probing chirality. However, due to polarization restriction, the conventional chiral SFG microscopes have mostly adopted noncollinear beam configurations, which only partially cover the aperture of microscope and strongly spoil the spatial resolution. In this study, we report the first experimental demonstration of collinear chiral SFG microscopy, which fundamentally supports diffraction-limited resolution. This advancement is attributed to the collinear focus of a radially polarized vectorial beam and a linearly polarized (LP) beam. The tightly focused vectorial beam has a very strong longitudinal component, which interacts with the LP beam and produces the chiral SFG. The collinear configuration can utilize the full aperture and thus push the spatial resolution close to the diffraction limit. This technique can potentially boost the understanding of chiral systems.

On-chip diffractive optical neural networks (DONNs) bring the advantages of parallel processing and low energy consumption. However, an accurate representation of the optical field’s evolution in the structure cannot be provided using the previous diffraction-based analysis method. Moreover, the loss caused by the open boundaries poses challenges to applications. A multimode DONN architecture based on a more precise eigenmode analysis method is proposed. We have constructed a universal library of input, output, and metaline structures utilizing this method, and realized a multimode DONN composed of the structures from the library. On the designed multimode DONNs with only one layer of the metaline, the classification task of an Iris plants dataset is verified with an accuracy of 90% on the blind test dataset, and the performance of the one-bit binary adder task is also validated. Compared to the previous architectures, the multimode DONN exhibits a more compact design and higher energy efficiency.

The optical rogue wave (RW), known as a short-lived extraordinarily high amplitude dynamics phenomenon with small appearing probabilities, plays an important role in revealing and understanding the fundamental physics of nonlinear wave propagations in optical systems. The random fiber laser (RFL), featured with cavity-free and “modeless” structure, has opened up new avenues for fundamental physics research and potential practical applications combining nonlinear optics and laser physics. Here, the extreme event of optical RW induced by noise-driven modulation instability that interacts with the cascaded stimulated Brillouin scattering, the quasi-phase-matched four-wave mixing as well as the random mode resonance process is observed in a Brillouin random fiber laser comb (BRFLC). Temporal and statistical characteristics of the RWs concerning their emergence and evolution are experimentally explored and analyzed. Specifically, temporally localized structures with high intensities including chair-like pulses with a sharp leading edge followed by a trailing plateau appear frequently in the BRFLC output, which can evolve to chair-like RW pulses with adjustable pulse duration and amplitude under controlled conditions. This investigation provides a deep insight into the extreme event of RWs and paves the way for RW manipulation for its generation and elimination in RFLs through adapted laser configuration.

A designed visual geometry group (VGG)-based convolutional neural network (CNN) model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements. Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format, probabilistic shaping, roll-off factor, baud rate, optical signal-to-noise ratio, and chromatic dispersion. The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios. Furthermore, the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network. Compared with the designed VGG-based CNN, the MobileNet-based MTL does not need to train all the classes, and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy, indicating great potential in various monitoring scenarios.

Non-line-of-sight (NLOS) imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections. This imaging method has garnered significant attention in diverse domains, including remote sensing, rescue operations, and intelligent driving, due to its wide-ranging potential applications. Nevertheless, accurately modeling the incident light direction, which carries energy and is captured by the detector amidst random diffuse reflection directions, poses a considerable challenge. This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging, which are crucial for achieving high-quality reconstructions. In this study, we propose a point spread function (PSF) model for the NLOS imaging system utilizing ray tracing with random angles. Furthermore, we introduce a reconstruction method, termed the physics-constrained inverse network (PCIN), which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network. The PCIN approach initializes the parameters randomly, guided by the constraints of the forward PSF model, thereby obviating the need for extensive training data sets, as required by traditional deep-learning methods. Through alternating iteration and gradient descent algorithms, we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters. The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups. Moreover, the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.

The pseudo-magnetic field, an artificial synthetic gauge field, has attracted intense research interest in the classical wave system. The strong pseudo-magnetic field is realized in a two-dimensional photonic crystal (PhC) by introducing the uniaxial linear gradient deformation. The emergence of the pseudo-magnetic field leads to the quantization of Landau levels. The quantum-Hall-like edge states between adjacent Landau levels are observed in our designed experimental implementation. The combination of two reversed gradient PhCs gives rise to the spatially nonuniform pseudo-magnetic field. The propagation of the large-area edge state and the interesting phenomenon of the snake state induced by the nonuniform pseudo-magnetic field is experimentally demonstrated in a PhC heterostructure. This provides a good platform to manipulate the transport of electromagnetic waves and to design useful devices for information processing.

A 60-mW solid-state deep ultraviolet (DUV) laser at 193 nm with narrow linewidth is obtained with two stages of sum frequency generation in LBO crystals. The pump lasers, at 258 and 1553 nm, are derived from a homemade Yb-hybrid laser employing fourth-harmonic generation and Er-doped fiber laser, respectively. The Yb-hybrid laser, finally, is power scaling by a 2 mm×2 mm×30 mm Yb:YAG bulk crystal. Accompanied by the generated 220-mW DUV laser at 221 nm, the 193-nm laser delivers an average power of 60 mW with a pulse duration of 4.6 ns, a repetition rate of 6 kHz, and a linewidth of ∼640 MHz. To the best of our knowledge, this is the highest power of 193- and 221-nm laser generated by an LBO crystal ever reported as well as the narrowest linewidth of 193-nm laser by it. Remarkably, the conversion efficiency reaches 27% for 221 to 193 nm and 3% for 258 to 193 nm, which are the highest efficiency values reported to date. We demonstrate the huge potential of LBO crystals for producing hundreds of milliwatt or even watt level 193-nm laser, which also paves a brand-new way to generate other DUV laser wavelengths.

The article provides information about the image on the cover of Advanced Photonics Nexus, Volume 3, Issue 2.