Optica

Optomechanical crystals (OMCs) enable coherent interactions between optical photons and microwave acoustic phonons, and represent a platform for implementing quantum transduction between microwave and optical signals. Optical-absorption-induced thermal noise at cryogenic (millikelvin) temperatures is one of the primary limitations of performance for OMC-based quantum transducers. Here, we address this challenge with a two-dimensional silicon OMC resonator that is side-coupled to a mechanically detached optical waveguide, realizing a six-fold reduction in the heating rate of the acoustic resonator compared to prior state-of-the-art, while operating in a regime of high optomechanical-backaction and millikelvin base temperature. This reduced heating translates into a demonstrated phonon-to-photon conversion efficiency of ${93.1} \pm {0.8}\%$ at an added noise of ${0.25} \pm {0.01}$ quanta, representing a significant advance toward quantum-limited microwave-optical frequency conversion and optically controlled quantum acoustic memories.

Chip-integrated optical memristors, modulating light in a nonvolatile and semicontinuous manner, are attractive to revolutionize on-chip optical signal processing via the constructions of nonvolatile reconfigurable photonic circuits, in-memory computing, brain-inspired architectures, etc. Mechanisms, including phase-change, filamentation, and ferroelectricity, have been attempted to implement on-chip optical memristors, though their intricate tradeoffs between fabrication compatibility, modulation depth, power consumption, retention time, and cyclability make it desired to pursue new architectures. Here, we demonstrate graphene-based on-chip optical amplitude and phase memristors by electrostatically doping the graphene integrated on a silicon nitride waveguide with a ferroelectric film. Benefiting from graphene’s significant dependence of complex refractive index on its carrier density and the ferroelectric remnant doping, semicontinuous nonvolatile modulation with a maximum depth of ${\sim}{32.5}\;{\rm dB}$ is realized with a low programming energy of {\sim}{1.86}\;{\rm pJ/}\unicode{x00B5}{\rm m}^2 , exhibiting good cyclability (fluctuation ratio ${\lt}{0.9}\%$ ) and long retention time (over 10 years). By integrating the graphene-based optical memristor with cascaded microring resonators, in-memory computings with multiple wavelength channels are demonstrated by analogue matrix-vector multiplication and digital logic gate operations. Combining these merits with CMOS-compatible on-chip graphene integration, the demonstrated graphene-based optical memristor has proven to be a competitive candidate for high-bandwidth neuromorphic computing, convolutional processing, and artificial intelligence on photonic integrated circuits.

Robust calibration of vector optically pumped magnetometers (OPMs) is a nontrivial task, but increasingly important for applications requiring high-accuracy such as magnetic navigation, geophysics research, and space exploration. Here, we showcase a vector OPM that utilizes Rabi oscillations driven between the hyperfine manifolds of $^{87}{\rm Rb}$ to measure the direction of a DC magnetic field against the polarization ellipse structure of a microwave field. By relying solely on atomic measurements—free-induction decay (FID) signals and Rabi measurements across multiple atomic transitions—this sensor can detect drift in the microwave vector reference and compensate for systematic shifts caused by off-resonant driving, nonlinear Zeeman (NLZ) effects, and buffer gas collisions. To facilitate deadzone-free operation, we also introduce a Rabi measurement that utilizes dressed-state resonances that appear during simultaneous Larmor precession and Rabi driving (SPaR). These measurements, performed within a microfabricated vapor cell platform, achieve an average vector accuracy of 0.46 mrad and vector sensitivities down to 11\;{\unicode{x00B5}}{\rm rad}/\sqrt {{\rm Hz}} for geomagnetic field strengths near 50 µT. This performance surpasses the challenging 1-deg (17 mrad) accuracy threshold of several contemporary OPM methods utilizing atomic vapors with an electromagnetic vector reference.

Spontaneous Raman scattering, also known as common old ordinary Raman scattering (COORS), is revisited to evaluate its applicability for hypersonic flow characterization. Due to its very low cross section, Raman scattering is often considered unsuitable for measuring low-pressure gas properties that are found in ground test simulations of high-altitude hypersonic flights. Utilizing a recently developed one-dimensional (1D) light scattering technique with a volume Bragg grating filter and Stokes sideband windowing, we demonstrate 1D rotational Raman measurements of temperature and neutral gas density across a bow shock in front of a blunt wedge model under Mach 6 hypersonic flow. The experiment was conducted in the Hypervelocity Expansion Tunnel at Texas A&M University. The measurements were successfully obtained during a single run of the tunnel operation, capturing the temperature and density distributions with dynamic ranges of 200–2000 K and $5 \times {10^{23}} - 4 \times {10^{24}} /{{\rm m}^3}$ , respectively, over both the free-stream and post-shock regions, covering approximately 10 mm in length with a spatial resolution of ${\lt}0.5\;{\rm mm}$ . Time-resolved high-speed measurement capability at 100 kHz was also demonstrated, showcasing the robustness of 1D COORS for gas diagnostics.

Optical imaging has traditionally relied on hardware to fulfill its imaging function, producing output measures that mimic the original objects. Developed separately, digital algorithms enhance or analyze these visual representations, rather than being integral to the imaging process. The emergence of computational optical imaging has blurred the boundary between hardware and algorithm, incorporating computation in silico as an essential step in producing the final image. It provides additional degrees of freedom in system design and enables unconventional capabilities and greater efficiency. This mini-review surveys various perspectives of such interactions between physical and digital layers. It discusses the representative works where dedicated algorithms join the specialized imaging modalities or pipelines to achieve images of unprecedented quality. It also examines the converse scenarios where hardware, such as optical elements and sensors, is engineered to perform image processing, partially or fully replacing computer-based counterparts. Finally, the review highlights the emerging field of end-to-end optimization, where optics and algorithms are co-designed using differentiable models and task-specific loss functions. Together, these advancements provide an overview of the current landscape of computational optical imaging, delineating significant progress while uncovering diverse directions and potential in this rapidly evolving field.

Polarization is a key parameter in light–matter interactions and is consequently closely linked to light manipulation, detection, and analysis. Terahertz (THz) waves, characterized by their broad bandwidth and long wavelength, pose significant challenges to efficient polarization control with existing technologies. Here, we leverage the mesoscale wavelength characteristics of THz waves and employ a mirror-coupled total internal reflection structure to mechanically modulate the phase difference between p- and s-waves by up to 289°. By incorporating a liquid crystal phase shifter to provide adaptive phase compensation, dispersion is eliminated across a broad bandwidth. We demonstrate active switching of orthogonal linear polarizations and handedness-selective quarter-wave conversions in the 1.6–3.4 THz range, achieving an average degree of linear/circular polarization exceeding 0.996. Furthermore, arbitrary polarization at any center frequency is achieved with a fractional bandwidth exceeding 90%. This customizable-bandwidth and multifunctional device offers an accurate and universal polarization control solution for various THz systems, paving the way for numerous polarization-sensitive applications.

We extend the concepts of Pancharatnam’s connection and phase difference of optical beams to cover random, stationary, partially polarized quasimonochromatic light. The geometric and dynamic structures of the phase are assessed within the framework of classical statistical polarization theory when the beam undergoes a cyclic, unitary polarization-state evolution. The existence of the two phase types is confirmed experimentally by conducting an interferometric measurement where the phase changes are found from the variations in the position of an intensity fringe pattern. The formalism also leads to a definition of the geometric phase for partially polarized beams, which is consistent with the results on mixed (qubit) quantum states.

Hollow-core fibers (HCFs) are inherently multimode, making it crucial to filter out higher-order modes (HOMs) within the shortest possible fiber length for applications such as high-speed coherent communications and fiber-optic gyroscopes. However, current HCF designs face the challenges of simultaneously achieving ultralow fundamental mode (FM) loss and ultrahigh HOM suppression. In this study, we present a fourfold truncated double-nested antiresonant nodeless hollow-core fiber (4T-DNANF) structure that addresses this challenge. Our 4T-DNANF enables greater control over phase matching between core modes and air modes in the cladding, allowing for minimized FM loss and substantially increased HOM loss. Experimentally, we fabricated several HCFs: one with an FM loss of 0.1 dB/km and an HOM loss of 430 dB/km, and another achieving an FM loss of 0.13 dB/km with a HOM loss of 6500 dB/km, yielding a higher-order mode extinction ratio of ${5} \times {{10}^4}$ —the highest reported to date.

The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but it requires nonvolatile photonic memory capable of rapid updates during on-chip training sessions or when new information becomes available during deployment. Phase-change materials (PCMs) are promising for providing compact, nonvolatile optical weighting; however, they face limitations in terms of bit precision, programming speed, and cycling endurance. Here, we propose a novel photonic memory cell that merges nonvolatile photonic weighting using PCMs with high-speed, volatile tuning enabled by an integrated PN junction. Our experiments demonstrate that the same PN modulator, fabricated via a foundry-compatible process, can achieve dual functionality. It supports coarse programmability for setting initial optical weights and facilitates high-speed fine-tuning to adjust these weights dynamically. The result shows a 400-fold increase in volatile tuning speed and a 10,000-fold enhancement in efficiency. This multifunctional photonic memory with volatile and nonvolatile capabilities could significantly advance the performance and versatility of photonic memory cells, providing robust solutions for dynamic computing environments.

Event cameras, also known as dynamic vision sensors, are an emerging modality for measuring fast dynamics asynchronously. Event cameras capture changes of log-intensity over time as a stream of “events” and generally cannot measure intensity itself; hence, they are only used for imaging dynamic scenes. However, fluctuations due to random photon arrival inevitably trigger noise events, even for static scenes. While previous efforts have been focused on filtering out these undesirable noise events to improve signal quality, we find that, in the photon-noise regime, these noise events are correlated with the static scene intensity. We analyze the noise event generation and model its relationship to illuminance. Based on this understanding, we propose a method, called Noise2Image, to leverage the illuminance-dependent noise characteristics to recover the static parts of a scene, which are otherwise invisible to event cameras. We experimentally collect a dataset of noise events on static scenes to train and validate Noise2Image. Our results show that Noise2Image can robustly recover intensity images solely from noise events, providing an approach for capturing static scenes in event cameras, without additional hardware.

Numerical modeling of diffraction between tilted planes provides remarkable flexibility in computational optics, enabling convenient prediction and manipulation of light on complicated geometries. Specifically it enables, for example, efficient simulation of wave propagation through lenses, fast calculation of holograms for meshed three-dimensional objects, and trapping particles in complicated shapes. However, computational accuracy and efficiency of existing methods are often at odds with each other. Here, we present an approach that accurately and efficiently models wave propagation between two arbitrary non-parallel planes, which is realized by rearranging the angular spectrum of the source field, coupled with a Fourier transform algorithm that does not require zero-padding and uniform sampling. It applies to both scalar and vectorial diffraction modeling, achieving a $10 - {10^9}$ times accuracy improvement, depending on different intersection angles. Notably, our method can cope well with orthogonal-plane diffraction, which is inaccessible in previous methods. Moreover, it enables a flexible balance between accuracy and efficiency, providing potential for further acceleration and accuracy enhancement. After theoretical verification, we provide experimental demonstration in computer-generated holography.

Quartz-enhanced photoacoustic spectroscopy (QEPAS) has become a versatile tool for detection of trace gases at extremely low concentrations, leveraging the high quality (Q)-factor of quartz tuning forks. However, this high Q-factor imposes an intrinsic spectral resolution limit for fast wavelength sweeping with tunable laser sources due to the long ringing time of the tuning fork. Here, we introduce a technique to coherently control and damp the tuning fork by phase-shifting the modulation sequences of the driving laser. Particularly, we send additional laser pulses into the photoacoustic cell with a timing that corresponds to a $\pi$ phase shift with respect to the tuning fork oscillation, effectively stopping its oscillatory motion. This enables acquisition of a complete methane spectrum spanning 3050–3450 nm in just three seconds, preserving the spectral shape. Our measured data is in good agreement with the theoretically expected spectra from the HITRAN database when convolved with the laser linewidth of ${\lt}2\;{{\rm cm}^{- 1}}$ . This will leverage the use of QEPAS with fast-sweeping OPOs in real-world gas sensing applications beyond laboratory environments with extremely fast acquisition speed enabled by our coherent control scheme.

The scattering and absorption of light within biological tissue severely limits the penetration depth of optical imaging techniques. Recently, it has been found that water-soluble, strongly absorbing dye molecules, such as tartrazine, can achieve in vivo tissue transparency by increasing the refractive index of aqueous components in tissue, as predicted by the Lorentz oscillator model and Kramers–Kronig relations. In this study, we topically applied absorbing dye molecules to the abdominal skin of pigmented and nonpigmented mice to enhance the penetration depth of optical coherence tomography (OCT) and photoacoustic microscopy (PAM). In both types of mice, the penetration depth of OCT was significantly improved using tartrazine and 4-aminoantipyrine. As predicted by the Kramers–Kronig relations and absorption spectra of the dyes, mice treated with 4-aminoantipyrine showed significantly improved penetration depth compared to mice treated with tartrazine for the PAM system with 532 nm excitation. These findings further demonstrate the use of absorbing dye molecules for achieving tissue transparency to enhance the penetration depth of depth-resolved optical imaging modalities in skin, thus accelerating the translation of these technologies in clinical areas, such as dermatology.

Scaling spectral broadening to higher pulse energies and average powers is a critical step in ultrafast science, especially for narrowband Yb-based solid state lasers, which have become the new state-of-the-art. Despite their high nonlinearity, molecular gases as a broadening medium inside hollow-core fibers have been limited to 25 W, at best. We demonstrate spectral broadening in nitrogen at ten-fold average powers up to 250 W with repetition rates from 25 to 200 kHz. The observed ten-fold spectral broadening is stronger compared to the more expensive krypton gas and enables pulse compression from 1.3 ps to 120 fs. We identified an intuitive explanation for the observed average power scaling based on the density of molecular ro-vibrational states of Raman active molecules. To verify this ansatz, the spectral broadening limitations in ${{\rm O}_2}$ and ${{\rm N}_2}{\rm O}$ are experimentally measured and agree well. On these grounds, we propose a perspective on the role, suitability, and limits of stimulated Raman scattering at high average and peak powers. Finally, high-harmonic generation is demonstrated at 200 kHz. These findings can have strong implications for intense, high-repetition-rate, pulsed ps laser propagation in the atmosphere where the dominant species are ${{\rm N}_2}$ and ${{\rm O}_2}$ .

We provide tandem Raman microscopy (TRAM), a cutting-edge multimodal microscope that integrates the methods of stimulated Raman scattering (SRS), coherent anti-Stokes Raman scattering (CARS), and spontaneous (resonance) Raman scattering [(R)RS]. The device facilitates sequential continuous wave (CW)-driven RS imaging to collect full spectra from every sample location and rapid pulsed-wave-driven SRS-CARS scanning at specific wavenumbers, offering a reliable and efficient analytical tool. The fingerprint spectral region can be included in the spectral imaging capabilities of CARS and SRS. Data collected from a sample area using several techniques can be integrated and analyzed, significantly increasing reliability and predictions. We analyzed the in vitro model of nonadherent leukocytes (LCs) to illustrate the capabilities of this unique system, emphasizing the benefits of measuring the same sample with three different Raman techniques without having to transfer it between microscopes. Data fusion allowed for the correct classification of two subtypes of LCs based on the partial least squares (PLS) discrimination, increasing the prediction accuracy from approximately 83% in the case of textural and morphological data (SRS) to 100% when combined with spectral data (SRS and RS). We also present RRS images of LC labeled with astaxanthin, and reference data from SRS and CARS microscopy. Additionally, polystyrene beads were investigated as a non-biological material. The advantages of each Raman technique are utilized when (R)RS, SRS, and CARS are combined into a single device. This paves the way for dependable chemical characterization in a wide range of scientific and industrial fields.