A three-dimensional (3D) waveguide model is applied in extreme ultraviolet (EUV) lithography simulations. The 3D waveguide model is equivalent to rigorous coupled-wave analysis, but fewer field components are used to solve Maxwell's equations. The 3D waveguide model uses two components of vector potential, and , corresponding to the two polarizations. The electric field of the polarization is approximately parallel to the axis, and the electric field of the polarization is approximately parallel to the axis. The 3D waveguide model solves a coupled vector wave equation for two polarizations. The refractive index of conventional EUV absorbers is close to that of vacuum. The weakly guiding approximation in optical fiber theory is applied to the 3D waveguide model. The coupled vector wave equations for the two polarizations are decoupled into two independent scalar wave equations. Maxwell's equations are simplified to a set of scalar wave equations. The weakly guiding approximation reduces the computation time to solve the equations. The computation time required to solve the weakly guiding approximation is about 1/5 of the time to solve the original 3D waveguide model.
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http://dx.doi.org/10.1364/JOSAA.516610 | DOI Listing |
We present a novel and efficient methodology for obtaining high-gain on-chip few-mode erbium-doped waveguide amplifiers, which exhibit a moderate differential mode gain (DMG). The efficiency of the device is validated by an optimized algorithm that theoretically models the gain performance of the six lowest-order optical modes, namely TE, TM, TE, TM, TE, and TM. Notably, these six signal modes achieve internal net gains exceeding 22 dB within a 5-cm-long waveguide, while maintaining the DMG at a mere 2 dB.
View Article and Find Full Text PDFJ Opt Soc Am A Opt Image Sci Vis
August 2024
A three-dimensional (3D) waveguide model is applied in extreme ultraviolet (EUV) lithography simulations. The 3D waveguide model is equivalent to rigorous coupled-wave analysis, but fewer field components are used to solve Maxwell's equations. The 3D waveguide model uses two components of vector potential, and , corresponding to the two polarizations.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Electrical and Computer Engineering, Duke University, Durham, North Carolina 27704, USA.
This paper addresses achieving the high time-bandwidth product necessary for low signal-to-noise ratio (SNR) target detection and localization in complex multipath environments. Time bandwidth product is often limited by dynamic environments and platform maneuvers. This paper introduces data-driven wideband focusing methods for passive sonar that optimize parameterized unitary matrices to align signal subspaces across the frequency band without relying on wave propagation models which are subject to mismatch in complex multipath environments.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
NASA Goddard Space Flight Center, 8800 Greenbelt Rd., Greenbelt, MD 20771, USA.
Polarization mode dispersion can introduce quantum decoherence in polarization encoded information, limiting the range of quantum communications protocols. Therefore, strategies to nullify the effect would reduce quantum decoherence and potentially increase the operational range of such technology. We constructed a quantum model of polarization mode dispersion alongside a two-level absorbing material.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
A complex-valued neural process method, combined with modal depth functions (MDFs) of the ocean waveguide, is proposed to reconstruct the acoustic field. Neural networks are used to describe complex Gaussian processes, modeling the distribution of the acoustic field at different depths. The network parameters are optimized through a meta-learning strategy, preventing overfitting under small sample conditions (sample size equals the number of array elements) and mitigating the slow reconstruction speed of Gaussian processes (GPs), while denoising and interpolating sparsely distributed acoustic field data, generating dense field data for virtual receiver arrays.
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