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Frontiers in Optics, FIO 2019 - Part of Frontiers in Optics + Laser science APS/DLS

作者： Yang, Ki Youl Skarda, Jinhie Cotrufo, Michele Ahn, Geun Ho Alù, Andrea Vučković, Jelena E. L. Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Department of Electrical Engineering City University of New York New YorkNY10031 United States

Broadband nonreciprocal transmission is demonstrated in an inverse-designed silicon photonic circuit. Cascaded nonlinear resonators break the single-resonator fundamental bound on forward transmission at wide nonrecip... 详细信息

ISBN: (纸本)9781943580675

关键词： Silicon photonics

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Efficient Focusing with Large Numerical Aperture Using a Hybrid Metalens

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Physical Review applied 2020年第4期13卷 044016-044016页

作者： Ming Kang Younes Ra'di Diego Farfan Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York 10031 USA Physics Program Graduate Center City University of New York New York 10016 USA Department of Electrical Engineering City College of The City University of New York New York 10031 USA

Focusing light using electrically thin layers is of paramount importance in several applications, from integrated optics to microwave engineering and sensing. Recently, gradient metasurfaces, which are electrically thin arrays of densely located polarizable particles, have been employed to perform different wave-front transformations, including focusing. In comparison to a bulk lens, these designs provide ultrathin geometries, but they suffer from fundamental limitations on their overall efficiency and achievable numerical aperture. Metagratings offer a solution for efficient beam steering at large angles, but it is challenging to utilize them in the small-angle limit. Here, we introduce a hybrid metalens design, which provides dramatic enhancement in lensing performance compared with that of state-of-the-art metasurfaces, combining metagratings and conventional gradient approaches. Our experimental prototype enables microwave focusing with large efficiency (η=0.479) and near-unity numerical aperture (NA=0.98), yielding a sharp focal point at the diffraction limit in the far field (FWHM=0.332λ). We propose a hybrid metalens design with exceptional performance in terms of efficiency and numerical aperture, opening up opportunities for high-throughput optical lithography, high-density data recording, focal plane arrays, radar, and communication systems.

关键词： Classical optics Integrated optics Metamaterials Optical & microwave phenomena Devices Surfaces

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Alternation of Magnetic Anisotropy Accompanied by Metal-Insulator Transition in Strained Ultrathin Manganite Heterostructures

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Physical Review applied 2021年第6期15卷 064019-064019页

作者： Masaki Kobayashi Le Duc Anh Masahiro Suzuki Shingo Kaneta-Takada Yukiharu Takeda Shin-ichi Fujimori Goro Shibata Arata Tanaka Masaaki Tanaka Shinobu Ohya Atsushi Fujimori Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Center for Spintronics Research Network The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Institute of Engineering Innovation Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan PRESTO JST 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Materials Sciences Research Center Japan Atomic Energy Agency Sayo-gun Hyogo 679-5148 Japan Department of Applied Physics Tokyo University of Science Katsushika Tokyo 125-8585 Japan Quantum Matter Program Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8530 Japan Department of Applied Physics Waseda University Okubo Shinjuku Tokyo 169-8555 Japan

A fundamental understanding of the interfacial magnetic properties in ferromagnetic heterostructures is essential for utilizing ferromagnetic materials for spintronic device applications. Here, we investigate the interfacial magnetic and electronic structures of epitaxial single-crystalline LaAlO3(LAO)/La0.6Sr0.4MnO3(LSMO)/Nb:SrTiO3(Nb:STO) heterostructures with varying LSMO layer thicknesses, in which the magnetic anisotropy strongly changes with the LSMO thickness due to the delicate balance between strains originating from both the Nb:STO and LAO layers, using x-ray magnetic circular dichroism and photoemission spectroscopy. We successfully detect the clear change of the magnetic behavior of the Mn ions concomitant with the thickness-dependent metal-insulator transition. Our results suggest that the double-exchange interaction induces ferromagnetism in the metallic LSMO film under tensile strain caused by the STO substrate, while the superexchange interaction determines the magnetic behavior in the insulating LSMO film under compressive strain originating from the top LAO layer. The change in strain, depending on LSMO layer thickness, is confirmed by scanning transmission electron microscopy. Based on those findings, the formation of a magnetic dead layer near the LAO/LSMO interface is attributed to competition between the superexchange interaction via Mn 3d3z2−r2 orbitals under compressive strain and the double-exchange interaction via the 3dx2−y2 orbitals. These findings provide key aspects of ferromagnetic oxide heterostructures for the development of spintronic device applications.

关键词： Electronic structure Ferromagnetism Magnetic anisotropy Metal-insulator transition Spintronics Transition metal oxides Photoemission spectroscopy X-ray magnetic circular dichroism

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Roadmap on Nonlocality in Photonic Materials and Metamaterials

arXiv

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arXiv 2025年

作者： Monticone, Francesco Mortensen, N. Asger Fernández-Domínguez, Antonio I. Luo, Yu Tserkezis, Christos Khurgin, Jacob B. Shahbazyan, Tigran V. Chaves, André J. Peres, Nuno M.R. Wegner, Gino Busch, Kurt Hu, Huatian della Sala, Fabio Zhang, Pu Ciracì, Cristian Aizpurua, Javier Babaze, Antton Borisov, Andrei G. Chen, Xue-Wen Christensen, Thomas Yan, Wei Yang, Yi Hohenester, Ulrich Huber, Lorenz Wubs, Martijn de Liberato, Simone Gonçalves, P.A.D. de Abajo, F. Javier García Hess, Ortwin Tarasenko, Illya Cox, Joel D. Jelver, Line Dias, Eduardo J.C. Sánchez, Miguel Sánchez Margetis, Dionisios Gómez-Santos, Guillermo Stauber, Tobias Tretyakov, Sergei Simovski, Constantin Pakniyat, Samaneh Gómez-Díaz, J. Sebastián Bondarev, Igor V. Biehs, Svend-Age Boltasseva, Alexandra Shalaev, Vladimir M. Krasavin, Alexey V. Zayats, Anatoly V. Alù, Andrea Song, Jung-Hwan Brongersma, Mark L. Levy, Uriel Long, Olivia Y. Guo, Cheng Fan, Shanhui Bozhevolnyi, Sergey I. Overvig, Adam Prudêncio, Filipa R. Silveirinha, Mário G. Gangaraj, S. Ali Hassani Argyropoulos, Christos Huidobro, Paloma A. Galiffi, Emanuele Yang, Fan Pendry, John B. Miller, David A.B. School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States POLIMA Center for Polariton-driven Light–Matter Interactions University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid MadridE-28049 Spain Universidad Autónoma de Madrid MadridE-28049 Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Physics Jackson State University JacksonMS39217 United States Department of Physics Aeronautics Institute of Technology SP So Jos dos Campos12228-900 Brazil Departamento de Física Universidade do Minho BragaP-4710-057 Portugal Av Mestre José Veiga Braga4715-330 Portugal Max-Born-Institut Berlin12489 Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik Berlin12489 Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 Arnesano73010 Italy Via Monteroni Campus Unisalento Lecce73100 Italy School of physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Donostia International Physics Center DIPC Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Electricity and Electronics University of the Basque Country Leioa48940 Spain Department of Applied Physics University of the Basque Country Donostia20018 Spain Université Paris-Saclay CNRS Institut des Scienc

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces–can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward–toward new scientific discoveries and technological advancements. Copyright © 2025, The Authors. All rights reserved.

关键词： Nanocrystals

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Automated osteoporosis classification and T-score prediction using hip radiographs via deep learning algorithm

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Therapeutic Advances in Musculoskeletal Disease 2024年 16卷

作者： Chen, Yu-Pin Chan, Wing P. Zhang, Han-Wei Tsai, Zhi-Ren Peng, Hsiao-Ching Huang, Shu-Wei Jang, Yeu-Chai Kuo, Yi-Jie Department of Orthopedics Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Department of Radiology Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Biomedica Corporation New Taipei City Taiwan Program for Aging China Medical University Taichung City Taiwan Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Electrical and Computer Engineering Institute of Electrical Control Engineering National Yang Ming Chiao Tung University Hsinchu County Hsinchu City Taiwan Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Center for Precision Medicine Research Asia University Taichung City Taiwan Department of Applied Science National Taitung University Taitung County Taitung City Taiwan Department of Orthopedics Wan Fang Hospital Taipei Medical University No. 111 Sec. 3 Xinglong Road Wenshan Taipei 11696 Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

Background: Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. Objectives: This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs. Design: This was a single-center, retrospective study. Methods: An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo. Results: The proposed screening tool illustrated a better performance (sensitivity: 97.2%;specificity: 95.6%;positive predictive value: 95.7%;negative predictive value: 97.1%;area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA (r = 0.996, p < 0.001). Conclusion: The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk. Trial registration: TMU-JIRB N201909036. © The Author(s), 2024.

关键词： deep learning neural network osteoporosis radiographs T-score

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Training of Physical Neural Networks

arXiv

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Routing Optical Spin and Pseudospin with Metasurfaces

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Physical Review applied 2020年第1期14卷 014029-014029页

作者： Yarden Mazor Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10026 USA

Controlling and guiding optical spins and pseudospins over an interface is of great importance in the emerging fields of spintronics and valleytronics, holding the promise for new degrees of freedom in the quest for efficient information transport. Here we explore the general possibilities offered by metasurfaces to route optical momentum, spin and helicity through surface waves. We show how anisotropy and bianisotropy can be engineered to impart extreme directionality to local spin excitations. While most previous works have focused on out-of-plane spins, in-plane excitations have become particularly important for valleytronic applications relying on two-dimensional materials, and they offer specific challenges addressed here with suitably engineered nonlocality in metasurfaces.

关键词： Metamaterials Optical vortices Photonics Valleytronics Devices Surfaces Transition metal dichalcogenides

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Exploiting Approximations in Supervisory Control with Distinguishers

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IFAC-PapersOnLine 2018年第7期51卷 13-18页

作者： Rosa, Marcelo Teixeira, Marcelo Malik, Robi Graduate Program in Electrical Engineering Federal University of Technology Paraná Pato Branco Brazil Department of Computer Science University of Waikato Hamilton New Zealand

Distinguishers have been used in Supervisory Control Theory (SCT) of Discrete Event Systems, as a way to simplify modeling tasks. Approximations complement this approach with an alternative to also reduce synthesis effort. Despite the advantages, supervisors obtained from approximated DES plants can be suboptimal, so that the modeling structure has to be reconstructed empirically, until the proper supervisor is found. In this paper we show how to construct suitable-approximations that always lead to least restrictive controllable synthesis. A manufacturing system example is provided to illustrate the approach. © 2018

关键词： Discrete event simulation Automation Control engineering Control theory Controllability Manufacture Models Supervisory personnel Controllable synthesis Distinguishers Modeling structures Modeling task Supervisory control Supervisory control theory

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THz Emission from Exchange-Coupled Fe/Ru/Ni Spintronic Emitters

THz Emission from Exchange-Coupled Fe/Ru/Ni Spintronic Emitt...

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International Conference on Infrared and Millimeter Waves

作者： R. Adam C. Greb D. E. Bürgler D. Cao S. Heidtfeld F. Wang J. Cheng D. Chakraborty I. Komissarov H. Hardtdegen M. Mikulics M. Büscher C. M. Schneider Roman Sobolewski Research Centre Jülich Peter Grünberg Institute (PGI-6) Jülich Germany Institut für Laser- und Plasmaphysik Heinrich Heine Universität Düsseldorf Düsseldorf Germany College of Physics Center for Marine Observation and Communications Qingdao University Qingdao China Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Department of Electrical and Computer Engineering University of Rochester Rochester New York USA Research Centre Jülich Ernst Ruska Centre for Microscopy and Spectroscopy with Electrons Jülich Germany Faculty of Physics University Duisburg-Essen Duisburg Germany Department of Physics University of California Davis Davis California USA

We explored THz emission from $\mathrm{Si}^{2} / \mathrm{SiO}_{2} / / \mathrm{Ta} / \mathrm{Fe} / \mathrm{Ru} /$ $\mathrm{Ni} / \mathrm{Al}_{2} \mathrm{O}_{3}$ spintronic emitters. We tuned magnetization alignment of Fe and Ni layers by varying the interlayer exchange coupling (IEC) strength using a range of Ru layer thickness t. Depending on IEC strength, magnetization hysteresis shows either ferromagnetic $(t=1.1 \mathrm{~nm}, 1.5 \mathrm{~nm})$, antiferromagnetic $(t=1.3 \mathrm{~nm})$ or canted $(t=1.7 \mathrm{~nm}, 1.9 \mathrm{~nm})$ relative alignment. Competition between IEC and an external magnetic field results in a dramatic difference in THz emission from the ferromagnetically (FM) and anti-ferromagnetically (AFM) coupled structures. The resulting THz emission from IEC structures is a result of an interference of THz transiens generated by the individual $\mathrm{Fe} / \mathrm{Ru}$ and $\mathrm{Ru} / \mathrm{Ni}$ emitters.

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Corrections to "iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and its Application to CT Organ Dosimetry"

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IEEE journal of biomedical and health informatics 2022年第1期26卷 478页

作者： Wanyi Fu Shobhit Sharma Ehsan Abadi Alexandros-Stavros Iliopoulos Qi Wang Joseph Y Lo Xiaobai Sun William P Segars Ehsan Samei Department of Electrical and Computer Engineering and Carl E. Ravin Advanced Imaging Laboratories Duke University Durham NC USA Department of Physics and Carl E. Ravin Advanced Imaging Laboratories Duke University Durham NC USA Department of Computer Science Duke University Durham NC USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Department of Radiology the Fourth Clinical Hospital of Hebei Medical University Heibei China Departments of Electrical and Computer Engineering Biomedical Engineering Medical Physics Graduate Program and Carl E. Ravin Advanced Imaging Laboratories Duke University Durham NC USA Medical Physics Graduate Program and Radiology and the Carl E. Ravin Advanced Imaging Laboratories Departments of Biomedical Engineering Duke University Durham NC USA Carl E. Ravin Advanced Imaging Laboratories Medical Physics Graduate Program Department of Radiology Electrical and Computer Engineering Biomedical Engineering and Physics Duke University Durham NC USA

In [1], the dose estimation accuracy using the alternative baseline method under modulated tube current was not correctly calculated due to an unintentional simulation error.

关键词： Phantoms Image segmentation Computed tomography Image registration

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