Broad-area lasers (BALs) have found applications in a variety of crucial fields on account of their high output power and high energy transfer efficiency. However, they suffer from poor spatial beam quality due to multi-mode behavior along the waveguide transverse direction. In this paper, we propose a novel metasurface waveguide structure acting as a transverse mode selective back-reflector for BALs. In order to effectively inverse design such a structure, a digital adjoint algorithm is introduced to adapt the considerably large design area and the high degree of freedom. As a proof of the concept, a device structure with a design area of 40 × 20 μm is investigated. The simulation results exhibit high fundamental mode reflection (above 90%), while higher-order transverse mode reflections are suppressed below 0.2%. This is, to our knowledge, the largest device structure designed based on the inverse method. We exploited such a device and the method and further investigated the device's robustness and feasibility of the inverse method. The results are elaborately discussed.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11085136 | PMC |
http://dx.doi.org/10.3390/nano14090787 | DOI Listing |
iScience
January 2025
Department of Artificial Intelligence, Hanyang University, Seoul 04763, South Korea.
We present a Fourier neural operator (FNO)-based surrogate solver for the efficient optimization of wavefronts in tunable metasurface controls. Existing methods, including the Gerchberg-Saxton algorithm and the adjoint optimization, are often computationally demanding due to their iterative processes, which require numerical simulations at each step. Our surrogate solver overcomes this limitation by providing highly accurate gradient estimations with respect to changes in tunable meta-atoms without the need for direct simulations.
View Article and Find Full Text PDFBioelectromagnetics
January 2025
Modular Implantable Neurotechnologies (MINE) Laboratory, Università Vita-Salute San Raffaele & Scuola Superiore Sant'Anna, Milan, Italy.
Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational models of neuromodulation can allow the exploration of large parameter spaces and the automatic optimization of stimulation devices and strategies, their high time complexity hinders their use on a large scale. We recently proposed the use of machine learning-based surrogate models to estimate the activation of nerve fibers under electrical stimulation, producing a considerable speed-up with respect to biophysically accurate models of fiber excitation while retaining good predictivity.
View Article and Find Full Text PDFNanophotonics
April 2024
School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
Finding an optimal device structure in the vast combinatorial design space of freeform nanophotonic design has been an enormous challenge. In this study, we propose physics-informed reinforcement learning (PIRL) that combines the adjoint-based method with reinforcement learning to improve the sample efficiency by an order of magnitude compared to conventional reinforcement learning and overcome the issue of local minima. To illustrate these advantages of PIRL over other conventional optimization algorithms, we design a family of one-dimensional metasurface beam deflectors using PIRL, exceeding most reported records.
View Article and Find Full Text PDFNeural Netw
November 2024
Université de Lorraine, CNRS, Institut Elie Cartan de Lorraine, Inria, BP 7023954506 Vandœuvre-lès-Nancy Cedex, France; Institut Universitaire de France (IUF), France. Electronic address:
In this work, we explore the numerical solution of geometric shape optimization problems using neural network-based approaches. This involves minimizing a numerical criterion that includes solving a partial differential equation with respect to a domain, often under geometric constraints like a constant volume. We successfully develop a proof of concept using a flexible and parallelizable methodology to tackle these problems.
View Article and Find Full Text PDFNanophotonics
March 2024
Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea.
Recent advances in nanotechnology have led to the emergence of metamaterials with unprecedented properties through precisely controlled topologies. To explore metamaterials with nanoscale topologies, interest in three-dimensional nanofabrication methods has grown and led to rapid production of target nanostructures over large areas. Additionally, inverse design methods have revolutionized materials science, enabling the optimization of microstructures and topologies to achieve the desired properties without extensive experimental cycles.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!