Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.00744

High-power photoconductive THz emitters for fast parallel electro-optic detection

Mohsen Khalili·Robin Löscher·Anna Martinez·Abhishek Singh·Stephan Winnerl·Clara J. Saraceno
View original source ↗Read source document ↗Export metadata ↓

Abstract

The terahertz (THz) region is highly attractive for imaging because it penetrates many optically opaque materials and can distinguish them by composition. Among the various methods that allow us to access this spectral region, THz time-domain spectroscopy (THz-TDS) is uniquely suited to retrieve broadband information, recording the full waveform of the THz electric field to recover both amplitude and phase with high sensitivity. Translating this capability into fast, wide-field imaging has however remained a major challenge, largely because the low average power of conventional TDS sources forces a trade-off between acquisition speed, field of view, and sensitivity. Here we demonstrate a hyperspectral THz camera in which a high-average-power, pulsed broadband THz source, delivering 38.3 mW at a 400 kHz repetition rate, drives parallel electro-optic detection over a wide field of view at high acquisition rates. The system simultaneously records the full THz waveform, in amplitude and phase, across 145,000 pixels over a ~14.5 mm field of view at an acquisition rate of 2 Hz. These results demonstrate the potential of high-average-power THz-TDS for broadband, high-speed imaging, opening applications such as inline material-resolved inspection and the spectroscopic imaging of irreversible, second-timescale processes such as hydration in biologically relevant systems.

Explore related subjects

physics.optics

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mohsen Khalili, Robin Löscher, Anna Martinez, Abhishek Singh, Stephan Winnerl, Clara J. Saraceno. 2026-09-30. High-power photoconductive THz emitters for fast parallel electro-optic detection. https://arxiv.org/abs/2610.00744

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Inverse Design of the Topology Bandwidth Tradeoff in Valley Photonic Crystals

Valley photonic crystals provide a route to broadband domain-wall guiding, but increasing the bulk gap can delocalize Berry curvature and reduce the non-quantized valley-Chern number. We introduce a mixed-integer particle-swarm framework that searches polygonal VPC unit cells and maps the Pareto tradeoff between normalized bandgap and valley topology using the bulk objective $\mathcal{T}=(Δf/f_0)^2|C_v|$. The selected designs are then evaluated independently using full-wave transport simulations through disorder-averaged propagation-length extraction in $200a$ domain-wall waveguides with positional disorder $σ=0.01a$. Across the studied design ensemble, the extracted propagation length exhibits a positive empirical association with $|C_v|$, providing a direct multi-design connection between the patch-integrated valley-Chern number and quantified disorder-limited transport in this VPC family. The framework therefore treats bandwidth, valley topology, and device robustness as distinct but connected quantities, enabling balanced inverse design rather than optimization of a single transport metric alone.

physics.optics↗
arXiv2026

Parallax Depth Sectioning and 3D Reconstruction in 4D-STEM

Three-dimensional (3D) information is encoded in four-dimensional scanning transmission electron microscopy (4D-STEM) through parallax between the virtual images formed at each detector pixel. In this paper we connect the encoding and retrieval of depth-dependent information in 4D-STEM to two other widely-used 3D imaging methods, tomography and light-field photography, and derive its 3D phase contrast transfer function. Viewing the 4D data as a collection of angular slices yields a collection of sinograms, providing an intuitive visual representation of the 3D information transfer. Within the small-angle approximation, tilt-corrected bright-field (tcBF) depth sectioning is equivalent to non-iterative tomographic reconstruction. Alternatively, the 4D data also can be mapped to light-field (or plenoptic) imaging, which acquires spatially resolved diffraction data in parallel using an array of lenslets. Adapting a light-field imaging algorithm, we show that tcBF depth sections can be obtained by extracting two-dimensional slices from a single four-dimensional fast Fourier transform (4D FFT) of the dataset, yielding a substantial speedup when reconstructing a large number of depth slices. These differing perspectives distinguish the geometric aspects of depth resolution from wave-optical effects that are present in phase-contrast imaging. Depth-resolved aberration-corrected bright-field (acBF) imaging yields improved 3D reconstructions compared to tcBF, removing contrast oscillations and reducing the variation in response with depth. We demonstrate acBF depth sectioning using simulated and experimental data and resolve distinct layers in stacked oxide films and nanoparticle assemblies. These non-iterative volumetric reconstructions from a single 4D-STEM dataset show promise for improved imaging of thick, weakly-scattering samples and for reconstructing low-dose acquisitions.

physics.optics↗
arXiv2026

Cross-platform frequency-domain physical neural networks with identical models and parameters

Frequency-domain physical neural networks (PNNs) have emerged as a promising analog computing paradigm, achieving a reduced number of devices, enhanced robustness, and high inference accuracy. However, existing demonstrations across various physical domains, such as optics, acoustics, and electronics, are mostly specific to the target devices or platforms, usually requiring hardware-dependent models and post-fabrication parameter tuning. Here, we demonstrate cross-platform implementations of frequency-domain PNN across different wave-based computing platforms using identical, pre-trained parameters. By exploiting shared second-order nonlinear processes, the PNN model is implemented on optical, microwave, and acoustic-wave platforms without the need of fine tuning the parameters. Evaluated on a unified four-class classification task, the frequency-domain PNN achieves high inference accuracies of 97.6% in optics, 98.4% in electronics, and 98.2% in mechanics. Further, we systematically compare related performance metrics across these three distinct physical domains, paving the way to a universal neural network across different physical platforms.

physics.optics↗