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Surface plasmons, the collective oscillation of electrons, enable the manipulation of optical fields with unprecedented spatial and time resolutions. They are the workhorse of a large set of applications, such as chemical/biological sensors or Raman scattering spectroscopy, to name only a few. In particular, the ultrafast optical response configures one of the most fundamental characteristics of surface plasmons. Thus, the rich physics about photon–electron interactions could be retrieved and studied in detail. The associated plasmon-enhanced electric fields, generated by focusing the surface plasmons far beyond the diffraction limit, allow reaching the strong field regime with relatively low input laser intensities. This is in clear contrast to conventional optical methods, where their intrinsic limitations demand the use of large and costly laser amplifiers, to attain high electric fields, able to manipulate the electron dynamics in the non-linear regime. Moreover, the coherent plasmonic field excited by the optical field inherits an ultrahigh precision that could be properly exploited in, for instance, ultraprecision spectroscopy. In this review, we summarize the research achievements and developments in ultrafast plasmonics over the last decade. We particularly emphasize the strong-field physics aspects and the ultraprecision spectroscopy using optical frequency combs.

Conventional machine vision systems based on RGB cameras struggle to distinguish materials that appear visually identical, such as plastics of the same color and shape. To address this limitation, we present a spectrometer-free time-division multiplexed (TDM) near-infrared (NIR) time-of-flight (ToF) vision system that enables simultaneous acquisition of spectral and geometric information using dual-detector architecture composed of an avalanche photodiode (APD) for multispectral reflectance detection and a single-photon avalanche diode (SPAD) for ToF ranging. By extending TDM to multispectral NIR imaging, the proposed system temporally separates nanosecond laser pulses at 980 nm, 1450 nm, and 1650 nm for material discrimination, while an additional 905 nm channel provides high-precision ToF depth mapping. This architecture eliminates bulky spectrometers and dispersive optics, minimizing optical loss while maintaining compactness and scalability. The system successfully recognizes 12 visually similar materials, including six white plastics, three green rubbers, and three silver metals, based on their unique NIR reflectance fingerprints encoded into false-color RGB images. A convolutional neural network (CNN) trained on these images achieves near-perfect classification accuracy. Furthermore, a dual-domain experiment with a mannequin and a human subject demonstrates simultaneous reconstruction of surface geometry and material differentiation under realistic conditions. This spectrometer-free multispectral ToF vision approach establishes a compact and efficient sensing platform for high-precision robotic perception, intelligent manufacturing, and physical artificial intelligence systems requiring both spectral and spatial awareness.

Complete characterization of ultrafast optical fields is essential for deploying femtosecond pulse sources in integrated photonic systems, yet conventional techniques rely on spectrally resolved detection and iterative phase retrieval. Recently, phase‐enabled nonlinear gating with unbalanced intensity (PENGUIN) showed that spectral phase information is preserved in an unbalanced nonlinear interferometric autocorrelation (IAC) enabling field retrieval without spectrally resolved detection. However, the original iterative reconstruction algorithm is only reliable for pulses near the Fourier‐transform limit. To overcome this dispersion limitation, we introduce DeepPENGUIN, a deep‐learning framework for direct spectral phase inference from unbalanced‐intensity IAC signals in the frequency domain without iterative retrieval. The model is trained on large‐scale synthetic data through physics‐informed supervised learning with spectral and mathematical constraints, providing a robust solution for the one‐dimensional phase retrieval problem for which no general inversion framework has been established. Validation using real‐world few‐cycle (8 fs) pulses demonstrates reliable phase retrieval over a large dispersion range from 0 to 200 fs2. A quantitative noise robustness analysis shows performance comparable to ptychographic frequency‐resolved optical gating across a broad range of noise levels. The feedforward inference architecture enables real‐time pulse reconstruction with millisecond‐level latency, providing a scalable route toward integrated ultrafast pulse diagnostics and feedback control. Phase‐enabled nonlinear gating with unbalanced intensity (PENGUIN) enables reconstruction of the complete ultrafast optical field from one‐dimensional nonlinear interferometric autocorrelation traces. Combined with deep learning, the DeepPENGUIN framework enables real‐time spectral phase retrieval without spectrally resolved detection or iterative algorithms while remaining robust under strong dispersion, highlighting its potential for compact and integrated ultrafast photonic systems.

The recent progress in the development of measurement systems for autonomous recognition had a substantial impact on emerging technology in numerous fields, especially robotics and automotive applications. In particular, time-of-flight (TOF) based light detection and ranging (LiDAR) systems enable to map the surrounding environmental information over long distances and with high accuracy. The combination of advanced LiDAR with an artificial intelligence platform allows enhanced object recognition and classification, which however still suffers from limitations of inaccuracy and misidentification. Recently, multi-spectral LiDAR systems have been employed to increase the object recognition performance by additionally providing material information in the short-wave infrared (SWIR) range where the reflection spectrum characteristics are typically very sensitive to material properties. However, previous multi-spectral LiDAR systems utilized band-pass filters or complex dispersive optical systems and even required multiple photodetectors, adding complexity and cost. In this work, we propose a time-division-multiplexing (TDM) based multi-spectral LiDAR system for semantic object inference by the simultaneous acquisition of spatial and spectral information. By utilizing the TDM method, we enable the simultaneous acquisition of spatial and spectral information as well as a TOF based distance map with minimized optical loss using only a single photodetector. Our LiDAR system utilizes nanosecond pulses of five different wavelengths in the SWIR range to acquire sufficient material information in addition to 3D spatial information. To demonstrate the recognition performance, we map the multi-spectral image from a human hand, a mannequin hand, a fabric gloved hand, a nitrile gloved hand, and a printed human hand onto an RGB-color encoded image, which clearly visualizes spectral differences as RGB color depending on the material while having a similar shape. Additionally, the classification performance of the multi-spectral image is demonstrated with a convolution neural network (CNN) model using the full multi-spectral data set. Our work presents a compact novel spectroscopic LiDAR system, which provides increased recognition performance and thus a great potential to improve safety and reliability in autonomous driving.

Plasmonic nanostructures allow the manipulation and confinement of optical fields on the sub-wavelength scale. The local field enhancement and environmentally sensitive resonance characteristics provided by these nanostructures are of high importance for biological and chemical sensing. Recently, surface lattice plasmon resonance (SLR) research has attracted much interest because of its superior quality factor ( -factor) compared to that of localized surface plasmon resonances (LSPR), which is facilitated by resonant plasmonic mode coupling between individual nanostructures over a large area. This advantage can be further enhanced by utilizing asymmetric 3D structures rather than low-height (typically height < ∼60 nm) structure arrays, which results in stronger coupling due to an increased mode volume. However, fabricating 3D, high-aspect ratio, symmetry-breaking structures is a complex and challenging process even with state-of-the-art fabrication technology. Here, we report a plasmonic metasurface of 3D inclined structures produced via commercial TEM grid–based stencil lithography with a -factor of 101.6, a refractive index sensitivity of 291 nm/RIU, and a figure of merit (FOM) of 44.7 in the visible wavelength range at a refractive index of 1.5 by utilizing the 3D SLR enhancement effect, which exceeds the performance of most LSPR systems ( < ∼10). The symmetry-breaking 3D inclined structures that are fabricated by electron beam evaporation at an angle increase the polarizability of the metasurface and the directionality of the diffractively scattered radiative field responsible for SLR mode coupling. Additionally, we explore the role of spatial coherence in facilitating the SLR effect and thus a high- plasmonic response from the nanostructures. Our work demonstrates the feasibility of producing 3D inclined structure arrays with pronounced SLR enhancement for high biological sensitivity by utilizing the previously unexplored inclined stencil lithography, which opens the way to fabricate highly sensitive plasmonic metasurfaces with this novel simple technique.

Photodetectors are crucial for modern technologies such as optical communications, imaging, autonomous vehicles, and machine vision. However, conventional semiconductor-based photodetectors require additional filtering systems due to their broad spectral response, leading to increased costs and complexity. Here, we present a narrow spectral response photodetector using hexagonally arranged plasmonic Au nanohole structures, eliminating the need for optical filters. The device achieves a full-width at half maximum (FWHM) bandwidth of ∼40 nm with a response peak at 760 nm and a linear photocurrent responsivity of 0.95 μA/W. The photothermoelectric effect, induced by the nonradiative decay of plasmonic resonance, converts optical radiation into an electric potential on the Au surface. The hexagonal nanohole design generates polarization-independent photocurrents and allows spectral tuning beyond the cutoff region of silicon photodetectors. This versatile approach enables customizable response characteristics across a broad wavelength range through geometric design, enhancing its potential for diverse applications.

Optical gain enhancement of two-dimensional CsPbBr 3 nanosheets was studied when the amplified spontaneous emission is guided by a patterned structure of polyurethane-acrylate. Given the uncertainties and pitfalls in retrieving a gain coefficient from the variable stripe length method, a gain contour g ( ℏ ω , x ) was obtained in the plane of spectrum energy (ℏ ω ) and stripe length ( x ), whereby an average gain was obtained, and gain saturation was analysed. Excitation and temperature dependence of the gain contour show that the waveguide enhances both gain and thermal stability due to the increased optical confinement and heat dissipation, and the gain origins were attributed to the two-dimensional excitons and the localized states. Optical gain of perovskite nanosheets becomes increased when a micro-patterned structure is utilized, where gain saturation is analyzed in the plane of spectrum energy and stripe length.

High-resolution spectroscopy unveils the fundamental physics of quantum states, molecular dynamics, and energy transfers. Ideally, a higher spectral resolution over a broader bandwidth is the prerequisite, but traditional spectroscopic techniques can only partially fulfill this requirement even with a bulky system. Here we report that a multi-frequency acousto-optic phase modulation at a chip-scale of soft polydimethylsiloxane can readily support a 200-times higher 0.5-MHz spectral resolution for the frequency-comb-based spectroscopy, while co-located plasmonic nanostructures mediate the strong light-matter interaction. These results suggest the potential of polydimethylsiloxane acousto-optic phase modulation for cost-effective, compact, multifunctional chip-scale tools in diverse applications such as quantum spectroscopy, high-finesse cavity analysis, and surface plasmonic spectroscopy.

Plasmonic nanoantennas have been extensively explored to boost nonlinear optical processes due to their capabilities to confine optical fields on the nanoscale. In harmonic generation, nanoantenna array architectures are often employed to increase the number of emitters in order to efficiently enhance the harmonic emission. A small laser focus spot on the nanoantenna array maximizes the harmonic yield since it scales nonlinearly with the incident laser intensity. However, the nonlinear yield of the nanoantennas lying at the boundary of a focused beam may exhibit significant deviations in comparison to those at the center of the beam due to the Gaussian intensity distribution of the beam. This spatial beam inhomogeneity can cause power instability of the emitted harmonics when the lateral beam position is not stable which we observed in plasmon-enhanced third-harmonic generation (THG). Hence, we propose a method for deterministically designing the density of a nanoantenna array to decrease the instability of the beam position-dependent THG yield. This method is based on reducing the ratio between the number of ambiguous nanoantennas located at the beam boundary and the total number of nanoantennas within the beam diameter to increase the plasmon-enhanced THG stability, which we term as the ratio of ambiguity ( ). We find that the coefficient of variation of the measured plasmonic THG yield enhancement decreases with the . Thus, our method is beneficial for designing reliable sensors or nonlinear optical devices consisting of nanoantenna arrays for enhancing output signals.

This study is aimed to evaluate and compare environmental impacts of various food waste management systems: anaerobic digestion, co-digestion with sewage sludge, and volume reduction using a garbage dryer followed by incineration from generation to final disposal. An environmental credit using life cycle assessment was employed to compare by-products. The entire disposal process, namely discharge, collection, transportation, treatment, and final disposal, was included in the system boundary. The functional unit was 1 tonne of food waste from households for each treatment option. Global warming potential of the category indicator was selected to assess the environmental impact of food waste disposal options. The net global warming potential (environmental credit) of the options (wet based) was 33 kg of carbon dioxide equivalent (CO 2 -eq) for anaerobic digestion and −315 kg of CO 2 -eq for incineration by renewable energy production as electricity, thermal energy, and primary materials avoided. We found that dryer-incineration option was an available alternative for food waste recycling in a metropolitan area in Korea.