arXiv subjects

Dan Wasserman

Publications and source records attributed to Dan Wasserman.

3 recordsLinked to original sources

arXiv2026

Uncooled low-noise thin-film optomechanical resonator for thermal sensing on lithium niobate

Optomechanical transduction harnesses the interaction between optical fields and mechanical motion to achieve sensitive measurement of weak mechanical quantities with inherently low noise. Lithium niobate combines low optical loss, strong piezoelectricity, high intrinsic fQ_m factor, and low thermal conductivity, making it promising for exploring optomechanical platforms targeting thermal sensing applications. Here, we developed an integrated optomechanical platform on thin-film lithium niobate with precisely engineered optical, mechanical, and thermal fields within a compact 40 {\mu}m by 40 {\mu}m footprint. The platform integrates suspended microring resonators with ultrathin central membranes, reducing mechanical stiffness and effective mass while maintaining a high optical factor Q_o of 1e6 and mechanical quality factor Q_m of 1117, which increases to 5.1e4 after oscillation. The design suppresses thermal dissipation into the silicon substrate and enhances thermal sensitivity, achieving a temperature coefficient of frequency of -124 ppm/K and a noise-equivalent power of 6.2 nW/sqrt(Hz) at 10 kHz at room temperature. This compact and scalable platform opens up new opportunities for high-sensitivity thermal sensing, supports heterogeneous integration with infrared absorbers for uncooled infrared detection, and enables fully integrated, all-optical on-chip readout, paving the way toward large-format, low-noise infrared sensing arrays.

physics.optics↗

arXiv2025

Subdiffractive confinement of ultrashort mid-IR pulses with photonic funnels

The ability to control the spatial distribution of light, particularly in deep sub-wavelength areas, is important for a range of materials science, microscopy, and communications applications. Separately, materials science and communications rely on the ability to temporally shape the evolution of electromagnetic pulses. In this work we investigate theoretically the propagation of ultrafast pulses inside hyperbolic metamaterials-based photonic funnels, which have been recently used to achieve deep subwavelength (wavelength/30) concentration of monochromatic mid-infrared light. By analyzing the complex spatio-temporal dynamics of the pulse-funnel interaction, we show that photonic funnels, in general, broaden bandwidth-limited ultrafast Gaussian pulses. We demonstrate that this broadening can be mitigated by pre-chirping the incoming light, realizing simultaneous intensity enhancement and spatio-temporal compression of mid-wave IR light in the all-semiconductor "designer metal" funnel platform. Our analysis suggests that, in combination with linear chirp, designer-metal-based photonic funnels can be utilized with 100 fs bandwidth- and diffraction-limited pulses to produce wavelength/30-scale signals of ~200 fs duration, with intensity enhancement on the order of 5. Lowering material absorption can further enhance the peak intensity. The results presented can be used to assess the perspectives of ultrafast sub-diffraction light manipulation in other portions of the electromagnetic spectrum by adjusting the (meta)material composition of the funnels.

physics.optics↗

arXiv2025

Physics-guided hierarchical neural networks for Maxwell's equations in plasmonic metamaterials

While machine learning (ML) has found multiple applications in photonics, traditional "black box" ML models typically require prohibitively large training data sets. Generation of such data, as well as the training processes themselves, consume significant resources, often limiting practical applications of ML. Here we demonstrate that embedding Maxwell's equations into ML design and training significantly reduces the required amount of data and improves the physics-consistency and generalizability of ML models, opening the road to practical ML tools that do not need extremely large training sets. The proposed physics-guided machine learning (PGML) approach is illustrated on the example of predicting complex field distributions within hyperbolic metamaterial photonic funnels, based on multilayered plasmonic-dielectric composites. The hierarchical network design used in this study enables knowledge transfer and points to the emergence of effective medium theories within neural networks.

physics.optics↗