Intelligent designs in nanophotonics: from optimization towards inverse creation
doi: 10.1186/s43074-021-00044-y
Intelligent designs in nanophotonics: from optimization towards inverse creation
-
摘要: Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at the heart of this topic, in which artificial intelligence (AI) furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance. In this review, we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms, manifesting a development trend from performance optimizations towards inverse creations of novel designs. To illustrate interplays between two fields, AI and photonics, we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles, and subsequently review their manifold usages among a set of popular meta-elements. As arranged from levels of individual optimized piece to practical system, we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits. We further comment on a set of open questions including reasonable applications of advanced algorithms, expensive data issue, and algorithm benchmarking, etc. Overall, we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.
-
关键词:
Abstract: Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years. Inverse design of functional nanostructures is at the heart of this topic, in which artificial intelligence (AI) furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance. In this review, we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms, manifesting a development trend from performance optimizations towards inverse creations of novel designs. To illustrate interplays between two fields, AI and photonics, we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles, and subsequently review their manifold usages among a set of popular meta-elements. As arranged from levels of individual optimized piece to practical system, we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits. We further comment on a set of open questions including reasonable applications of advanced algorithms, expensive data issue, and algorithm benchmarking, etc. Overall, we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.-
Key words:
- Artificial intelligence /
- Nanophotonics /
- Metasurface /
- Optical neural network /
- Intelligent designs
-
[1] Novotny L, Hecht B. Principles of Nano-optics. Cambridge: Cambridge University Press; 2012. [2] Maier SA. Plasmonics: Fundamentals and Applications. Berlin: Springer; 2007. [3] Lin S-y, Fleming J, Hetherington D, Smith B, Biswas R, Ho K, Sigalas M, Zubrzycki W, Kurtz S, Bur J. A three-dimensional photonic crystal operating at infrared wavelengths. Nature. 1998; 394(6690):251–53. [4] Russell P. Photonic crystal fibers. Science. 2003; 299(5605):358–62. [5] Maier SA, Brongersma ML, Kik PG, Meltzer S, Requicha AA, Atwater HA. Plasmonics - a route to nanoscale optical devices. Adv Mater. 2001; 13(19):1501–05. [6] Barnes WL, Dereux A, Ebbesen TW. Surface plasmon subwavelength optics. Nature. 2003; 424(6950):824–30. [7] Smith DR, Padilla WJ, Vier D, Nemat-Nasser SC, Schultz S. Composite medium with simultaneously negative permeability and permittivity. Phys Rev Lett. 2000; 84(18):4184. [8] Yan M, Ruan Z, Qiu M. Scattering characteristics of simplified cylindrical invisibility cloaks. Opt Express. 2007; 15(26):17772–82. [9] Zhang B, Luo Y, Liu X, Barbastathis G. Macroscopic invisibility cloak for visible light. Phys Rev Lett. 2011; 106(3):033901. [10] Campbell SD, Sell D, Jenkins RP, Whiting EB, Fan JA, Werner DH. Review of numerical optimization techniques for meta-device design. Opt Mater Express. 2019; 9(4):1842–63. [11] Malkiel I, Mrejen M, Nagler A, Arieli U, Wolf L, Suchowski H. Plasmonic nanostructure design and characterization via deep learning. Light: Sci Appl. 2018; 7(1):1–8. [12] So S, Badloe T, Noh J, Rho J, Bravo-Abad J. Deep learning enabled inverse design in nanophotonics. Nanophotonics. 2020; 9(5):1041–57. [13] Russell S, Norvig P. Artificial Intelligence: a Modern Approach, 4th Ed. Hoboken: Pearson Education, Inc.; 2020. [14] corporation N. Difference Between AI, Machine Learning, and Deep Learning. 2021. https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/. Accessed 28 June 2021. [15] Elsawy MM, Lanteri S, Duvigneau R, Fan JA, Genevet P. Numerical optimization methods for metasurfaces. Laser Photon Rev. 2020; 14(10):1900445. [16] Rho J, Fan JA. Freeform metasurface design based on topology optimization. MRS Bull. 2020; 45(3):196–201. [17] Lin Z, Liu V, Pestourie R, Johnson SG. Topology optimization of freeform large-area metasurfaces. Opt Express. 2019; 27(11):15765–75. [18] Zhang Q, Yu H, Barbiero M, Wang B, Gu M. Artificial neural networks enabled by nanophotonics. Light: Sci Appl. 2019; 8(1):1–14. [19] Ma W, Liu Z, Kudyshev ZA, Boltasseva A, Cai W, Liu Y. Deep learning for the design of photonic structures. Nat Photon. 2021; 15(2):77–90. [20] Jiang J, Chen M, Fan JA. Deep neural networks for the evaluation and design of photonic devices. Nat Rev Mater. 2020:1–22. [21] Offrein BJ, Bona G-L, Germann R, Massarek I, Erni D, et al. A very short planar silica spot-size converter using a nonperiodic segmented waveguide. J Lightwave Technol. 1998; 16(9):1680. [22] Dobson DC, Cox SJ. Maximizing band gaps in two-dimensional photonic crystals. SIAM J Appl Math. 1999; 59(6):2108–20. [23] Borel PI, Harpøth A, Frandsen LH, Kristensen M, Shi P, Jensen JS, Sigmund O. Topology optimization and fabrication of photonic crystal structures. Opt Express. 2004; 12(9):1996–2001. [24] Lalau-Keraly CM, Bhargava S, Miller OD, Yablonovitch E. Adjoint shape optimization applied to electromagnetic design. Opt Express. 2013; 21(18):21693–701. [25] Molesky S, Lin Z, Piggott AY, Jin W, Vucković J, Rodriguez AW. Inverse design in nanophotonics. Nat Photonics. 2018; 12(11):659–70. [26] Piggott AY, Lu J, Lagoudakis KG, Petykiewicz J, Babinec TM, Vučković J. Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer. Nat Photonics. 2015; 9(6):374–77. [27] Ma W, Cheng F, Liu Y. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano. 2018; 12(6):6326–34. [28] Lin X, Rivenson Y, Yardimci NT, Veli M, Luo Y, Jarrahi M, Ozcan A. All-optical machine learning using diffractive deep neural networks. Science. 2018; 361(6406):1004–08. [29] Liu C, Maier SA, Li G. Genetic-algorithm-aided meta-atom multiplication for improved absorption and coloration in nanophotonics. ACS Photonics. 2020; 7(7):1716–22. [30] Bonod N, Bidault S, Burr GW, Mivelle M. Evolutionary optimization of all-dielectric magnetic nanoantennas. Adv Opt Mater. 2019; 7(10):1900121. [31] Li Z, Stan L, Czaplewski DA, Yang X, Gao J. Broadband infrared binary-pattern metasurface absorbers with micro-genetic algorithm optimization. Opt Lett. 2019; 44(1):114–17. [32] Pogrebnyakov AV, Bossard JA, Turpin JP, Musgraves JD, Shin HJ, Rivero-Baleine C, Podraza N, Richardson KA, Werner DH, Mayer TS. Reconfigurable near-ir metasurface based on ge 2 sb 2 te 5 phase-change material. Opt Mater Express. 2018; 8(8):2264–75. [33] Li Z, Rosenmann D, Czaplewski DA, Yang X, Gao J. Strong circular dichroism in chiral plasmonic metasurfaces optimized by micro-genetic algorithm. Opt Express. 2019; 27(20):28313–23. [34] Forestiere C, Donelli M, Walsh GF, Zeni E, Miano G, Dal Negro L. Particle-swarm optimization of broadband nanoplasmonic arrays. Opt Lett. 2010; 35(2):133–35. [35] Christiansen RE, Michon J, Benzaouia M, Sigmund O, Johnson SG. Inverse design of nanoparticles for enhanced raman scattering. Opt Express. 2020; 28(4):4444–62. [36] Li Y, Xu Y, Jiang M, Li B, Han T, Chi C, Lin F, Shen B, Zhu X, Lai L, et al. Self-learning perfect optical chirality via a deep neural network. Phys Rev Lett. 2019; 123(21):213902. [37] Tao Z, You J, Zhang J, Zheng X, Liu H, Jiang T. Optical circular dichroism engineering in chiral metamaterials utilizing a deep learning network. Opt Lett. 2020; 45(6):1403–06. [38] Gao L, Li X, Liu D, Wang L, Yu Z. A bidirectional deep neural network for accurate silicon color design. Adv Mater. 2019; 31(51):1905467. [39] Li X, Shu J, Gu W, Gao L. Deep neural network for plasmonic sensor modeling. Opt Mater Express. 2019; 9(9):3857–62. [40] Hemmatyar O, Abdollahramezani S, Kiarashinejad Y, Zandehshahvar M, Adibi A. Full color generation with fano-type resonant hfo 2 nanopillars designed by a deep-learning approach. Nanoscale. 2019; 11(44):21266–74. [41] Ma W, Cheng F, Xu Y, Wen Q, Liu Y. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy. Adv Mater. 2019; 31(35):1901111. [42] Liu Z, Zhu D, Rodrigues SP, Lee K-T, Cai W. Generative model for the inverse design of metasurfaces. Nano Lett. 2018; 18(10):6570–76. [43] So S, Rho J. Designing nanophotonic structures using conditional deep convolutional generative adversarial networks. Nanophotonics. 2019; 8(7):1255–61. [44] Sajedian I, Badloe T, Rho J. Optimisation of colour generation from dielectric nanostructures using reinforcement learning. Opt Express. 2019; 27(4):5874–83. [45] Yao K, Unni R, Zheng Y. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale. Nanophotonics. 2019; 8(3):339–66. [46] Liu Z, Zhu D, Raju L, Cai W. Tackling Photonic Inverse Design with Machine Learning. Adv Sci. 2021; 8(5):2002923. [47] Xu Y, Zhang X, Fu Y, Liu Y. Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks. Photon Res. 2021; 9(4):135–52. [48] Wiecha PR, Arbouet A, Girard C, Muskens OL. Deep learning in nano-photonics: inverse design and beyond. Photon Res. 2021; 9(5):182–200. [49] Christiansen RE, Sigmund O. A tutorial for inverse design in photonics by topology optimization. arXiv preprint arXiv:2008.11816. 2020. [50] Ma L, Li J, Liu Z, Zhang Y, Zhang N, Zheng S, Lu C. Intelligent algorithms: new avenues for designing nanophotonic devices. Chin Opt Lett. 2021; 19(1):011301. [51] Christiansen RE, Sigmund O. Compact 200 line matlab code for inverse design in photonics by topology optimization: tutorial. J Opt Soc Am B. 2021; 38(2):510–20. [52] Wetzstein G, Ozcan A, Gigan S, Fan S, Englund D, Soljačić M, Denz C, Miller DA, Psaltis D. Inference in artificial intelligence with deep optics and photonics. Nature. 2020; 588(7836):39–47. [53] Zhou J, Huang B, Yan Z, Bünzli J-CG. Emerging role of machine learning in light-matter interaction. Light: Sci Appl. 2019; 8(1):1–7. [54] Brown KA, Brittman S, Maccaferri N, Jariwala D, Celano U. Machine learning in nanoscience: Big data at small scales. Nano Lett. 2019; 20(1):2–10. [55] Shastri BJ, Tait AN, de Lima TF, Pernice WH, Bhaskaran H, Wright CD, Prucnal PR. Photonics for artificial intelligence and neuromorphic computing. Nat Photon. 2021; 15(2):102–14. [56] Meinzer N, Barnes WL, Hooper IR. Plasmonic meta-atoms and metasurfaces. Nat Photon. 2014; 8(12):889. [57] Weile DS, Michielssen E. Genetic algorithm optimization applied to electromagnetics: A review. IEEE Trans Antennas Propag. 1997; 45(3):343–53. [58] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553):436–44. [59] Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep Learning vol. 1: MIT press Cambridge; 2016. [60] Bendsoe MP, Sigmund O. Topology Optimization: Theory, Methods, and Applications. Berlin: Springer; 2013. [61] Jensen JS, Sigmund O. Topology optimization for nano-photonics. Laser Photonics Rev. 2011; 5(2):308–21. [62] Schneider P-I, Garcia Santiago X, Soltwisch V, Hammerschmidt M, Burger S, Rockstuhl C. Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction. ACS Photonics. 2019; 6(11):2726–33. [63] Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN’95-international Conference on Neural Networks, vol. 4. IEEE: 1995. p. 1942–48. [64] Shi Y, et al. Particle swarm optimization: developments, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), vol. 1. IEEE: 2001. p. 81–86. [65] Christiansen RE, Sigmund O. Inverse design in photonics by topology optimization: tutorial. J Opt Soc Am B. 2021; 38(2):496–509. [66] Sigmund O. A 99 line topology optimization code written in matlab. Struct Multidiscip Optim. 2001; 21(2):120–27. [67] Ferrari F, Sigmund O. A new generation 99 line matlab code for compliance topology optimization and its extension to 3d. Struct Multidiscip Optim. 2020; 62(4):2211–28. [68] Doersch C. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908. 2016. [69] Badloe T, Kim I, Rho J. Biomimetic ultra-broadband perfect absorbers optimised with reinforcement learning. Phys Chem Chem Phys. 2020; 22(4):2337–42. [70] Sajedian I, Lee H, Rho J. Double-deep q-learning to increase the efficiency of metasurface holograms. Sci Rep. 2019; 9(1):1–8. [71] Liang H, Lin Q, Xie X, Sun Q, Wang Y, Zhou L, Liu L, Yu X, Zhou J, Krauss TF, et al. Ultrahigh numerical aperture metalens at visible wavelengths. Nano Lett. 2018; 18(7):4460–66. [72] Phan T, Sell D, Wang EW, Doshay S, Edee K, Yang J, Fan JA. High-efficiency, large-area, topology-optimized metasurfaces. Light: Sci Appl. 2019; 8(1):1–9. [73] Christiansen RE, Lin Z, Roques-Carmes C, Salamin Y, Kooi SE, Joannopoulos JD, Soljačić M, Johnson SG. Fullwave maxwell inverse design of axisymmetric, tunable, and multi-scale multi-wavelength metalenses. Opt Express. 2020; 28(23):33854–68. [74] Mansouree M, Kwon H, Arbabi E, McClung A, Faraon A, Arbabi A. Multifunctional 2.5 d metastructures enabled by adjoint optimization. Optica. 2020; 7(1):77–84. [75] Zou X, Zheng G, Yuan Q, Zang W, Chen R, Li T, Li L, Wang S, Wang Z, Zhu S. Imaging based on metalenses. PhotoniX. 2020; 1(1):1–24. [76] Fan Y, Xu Y, Qiu M, Jin W, Zhang L, Lam EY, Tsai DP, Lei D. Phase-controlled metasurface design via optimized genetic algorithm. Nanophotonics. 2020; 9(12):3931–9. [77] Meem M, Banerji S, Pies C, Oberbiermann T, Majumder A, Sensale-Rodriguez B, Menon R. Large-area, high-numerical-aperture multi-level diffractive lens via inverse design. Optica. 2020; 7(3):252–53. [78] Banerji S, Meem M, Majumder A, Sensale-Rodriguez B, Menon R. Imaging over an unlimited bandwidth with a single diffractive surface. arXiv preprint arXiv:1907.06251. 2019. [79] Banerji S, Meem M, Majumder A, Sensale-Rodriguez B, Menon R. Extreme-depth-of-focus imaging with a flat lens. Optica. 2020; 7(3):214–17. [80] Chung H, Miller OD. High-na achromatic metalenses by inverse design. Opt Express. 2020; 28(5):6945–65. [81] Lin Z, Johnson SG. Overlapping domains for topology optimization of large-area metasurfaces. Opt Express. 2019; 27(22):32445–53. [82] Lin Z, Johnson SG. Topology-optimized nanostructures for high-na lensing optics. In: 2020 International Applied Computational Electromagnetics Society Symposium (ACES). IEEE: 2020. p. 1–2. [83] Bayati E, Pestourie R, Colburn S, Lin Z, Johnson SG, Majumdar A. Inverse designed metalenses with extended depth of focus. ACS Photon. 2020; 7(4):873–78. [84] Mansouree M, McClung A, Samudrala S, Arbabi A. Large-scale parametrized metasurface design using adjoint optimization. ACS Photon. 2021; 8(2):455–63. [85] Pestourie R, Mroueh Y, Nguyen TV, Das P, Johnson SG. Active learning of deep surrogates for pdes: Application to metasurface design. npj Comput Mater. 2020; 6(1):1–7. [86] Sell D, Yang J, Doshay S, Yang R, Fan JA. Large-angle, multifunctional metagratings based on freeform multimode geometries. Nano Lett. 2017; 17(6):3752–57. [87] Jiang J, Sell D, Hoyer S, Hickey J, Yang J, Fan JA. Free-form diffractive metagrating design based on generative adversarial networks. ACS Nano. 2019; 13(8):8872–78. [88] Liu Z, Zhu D, Lee K-T, Kim AS, Raju L, Cai W. Compounding meta-atoms into metamolecules with hybrid artificial intelligence techniques. Adv Mater. 2020; 32(6):1904790. [89] Chung H, Miller OD. Tunable metasurface inverse design for 80% switching efficiencies and 144 angular deflection. ACS Photon. 2020; 7(8):2236–43. [90] Khorasaninejad M, Capasso F. Broadband multifunctional efficient meta-gratings based on dielectric waveguide phase shifters. Nano Lett. 2015; 15(10):6709–15. [91] Elsawy MM, Lanteri S, Duvigneau R, Brière G, Mohamed MS, Genevet P. Global optimization of metasurface designs using statistical learning methods. Sci Rep. 2019; 9(1):1–15. [92] Jafar-Zanjani S, Inampudi S, Mosallaei H. Adaptive genetic algorithm for optical metasurfaces design. Sci Rep. 2018; 8(1):1–16. [93] Inampudi S, Mosallaei H. Neural network based design of metagratings. Appl Phys Lett. 2018; 112(24):241102. [94] Jiang J, Fan JA. Global optimization of dielectric metasurfaces using a physics-driven neural network. Nano Lett. 2019; 19(8):5366–72. [95] Wen F, Jiang J, Fan JA. Robust freeform metasurface design based on progressively growing generative networks. ACS Photon. 2020; 7(8):2098–104. [96] Jiang J, Fan JA. Simulator-based training of generative neural networks for the inverse design of metasurfaces. Nanophotonics. 2020; 9(5):1059–69. [97] Chen M, Jiang J, Fan JA. Design space reparameterization enforces hard geometric constraints in inverse-designed nanophotonic devices. ACS Photon. 2020; 7(11):3141–51. [98] Dory C, Vercruysse D, Yang KY, Sapra NV, Rugar AE, Sun S, Lukin DM, Piggott AY, Zhang JL, Radulaski M, et al.Inverse-designed diamond photonics. Nat Commun. 2019; 10(1):1–7. [99] Gostimirovic D, Winnie NY. An open-source artificial neural network model for polarization-insensitive silicon-on-insulator subwavelength grating couplers. IEEE J Sel Top Quantum Electron. 2018; 25(3):1–5. [100] Melati D, Grinberg Y, Dezfouli MK, Janz S, Cheben P, Schmid JH, Sánchez-Postigo A, Xu D-X. Mapping the global design space of nanophotonic components using machine learning pattern recognition. Nat Commun. 2019; 10(1):1–9. [101] Jin W, Molesky S, Lin Z, Fu K-MC, Rodriguez AW. Inverse design of compact multimode cavity couplers. Opt Express. 2018; 26(20):26713–21. [102] Su L, Trivedi R, Sapra NV, Piggott AY, Vercruysse D, Vučković J. Fully-automated optimization of grating couplers. Opt Express. 2018; 26(4):4023–34. [103] Sapra NV, Vercruysse D, Su L, Yang KY, Skarda J, Piggott AY, Vučković J. Inverse design and demonstration of broadband grating couplers. IEEE J Sel Top Quantum Electron. 2019; 25(3):1–7. [104] Jin W, Li W, Orenstein M, Fan S. Inverse design of lightweight broadband reflector for relativistic lightsail propulsion. ACS Photon. 2020; 7(9):2350–55. [105] Hughes TW, Minkov M, Williamson IA, Fan S. Adjoint method and inverse design for nonlinear nanophotonic devices. ACS Photon. 2018; 5(12):4781–87. [106] Tahersima MH, Kojima K, Koike-Akino T, Jha D, Wang B, Lin C, Parsons K. Deep neural network inverse design of integrated photonic power splitters. Sci Rep. 2019; 9(1):1–9. [107] Liu Z, Liu X, Xiao Z, Lu C, Wang H-Q, Wu Y, Hu X, Liu Y-C, Zhang H, Zhang X. Integrated nanophotonic wavelength router based on an intelligent algorithm. Optica. 2019; 6(10):1367–73. [108] Lu C, Liu Z, Wu Y, Xiao Z, Yu D, Zhang H, Wang C, Hu X, Liu Y-C, Liu X, et al. Nanophotonic polarization routers based on an intelligent algorithm. Adv Opt Mater. 2020; 8(10):1902018. [109] Su L, Vercruysse D, Skarda J, Sapra NV, Petykiewicz JA, Vučković J. Nanophotonic inverse design with spins: Software architecture and practical considerations. Appl Phys Rev. 2020; 7(1):011407. [110] Piggott AY, Petykiewicz J, Su L, Vučković J. Fabrication-constrained nanophotonic inverse design. Sci Rep. 2017; 7(1):1–7. [111] Zangeneh-Nejad F, Sounas DL, Alù A, Fleury R. Analogue computing with metamaterials. Nat Rev Mater. 2020:1–19. [112] Marković D, Mizrahi A, Querlioz D, Grollier J. Physics for neuromorphic computing. Nat Rev Phys. 2020; 2(9):499–510. [113] Xu R, Lv P, Xu F, Shi Y. A survey of approaches for implementing optical neural networks. Opt Laser Technol. 2021; 136:106787. [114] Liu J, Wu Q, Sui X, Chen Q, Gu G, Wang L, Li S. Research progress in optical neural networks: theory, applications and developments. PhotoniX. 2021; 2(1):1–39. [115] Shen Y, Harris NC, Skirlo S, Prabhu M, Baehr-Jones T, Hochberg M, Sun X, Zhao S, Larochelle H, Englund D, et al.Deep learning with coherent nanophotonic circuits. Nat Photon. 2017; 11(7):441. [116] Hughes TW, Minkov M, Shi Y, Fan S. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica. 2018; 5(7):864–71. [117] Qu Y, Zhu H, Shen Y, Zhang J, Tao C, Ghosh P, Qiu M. Inverse design of an integrated-nanophotonics optical neural network. Sci Bull. 2020; 65(14):1177–83. [118] Khoram E, Chen A, Liu D, Ying L, Wang Q, Yuan M, Yu Z. Nanophotonic media for artificial neural inference. Photon Res. 2019; 7(8):823–27. [119] Harris NC, Carolan J, Bunandar D, Prabhu M, Hochberg M, Baehr-Jones T, Fanto ML, Smith AM, Tison CC, Alsing PM, et al.Linear programmable nanophotonic processors. Optica. 2018; 5(12):1623–31. [120] Estakhri NM, Edwards B, Engheta N. Inverse-designed metastructures that solve equations. Science. 2019; 363(6433):1333–38. [121] Hamerly R, Bernstein L, Sludds A, Soljačić M, Englund D. Large-scale optical neural networks based on photoelectric multiplication. Phys Rev X. 2019; 9(2):021032. [122] Miscuglio M, Sorger VJ. Photonic tensor cores for machine learning. Appl Phys Rev. 2020; 7(3):031404. [123] Zhou T, Fang L, Yan T, Wu J, Li Y, Fan J, Wu H, Lin X, Dai Q. In situ optical backpropagation training of diffractive optical neural networks. Photon Res. 2020; 8(6):940–53. [124] Yan T, Wu J, Zhou T, Xie H, Xu F, Fan J, Fang L, Lin X, Dai Q. Fourier-space diffractive deep neural network. Phys Rev Lett. 2019; 123(2):023901. [125] Qian C, Lin X, Lin X, Xu J, Sun Y, Li E, Zhang B, Chen H. Performing optical logic operations by a diffractive neural network. Light: Sci Appl. 2020; 9(1):1–7. [126] Sui X, Wu Q, Liu J, Chen Q, Gu G. A review of optical neural networks. IEEE Access. 2020; 8:70773–83. [127] Li J, Mengu D, Luo Y, Rivenson Y, Ozcan A. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv Photon. 2019; 1(4):046001. [128] Luo Y, Mengu D, Yardimci NT, Rivenson Y, Veli M, Jarrahi M, Ozcan A. Design of task-specific optical systems using broadband diffractive neural networks. Light: Sci Appl. 2019; 8(1):1–14. [129] Dou H, Deng Y, Yan T, Wu H, Lin X, Dai Q. Residual d 2 nn: training diffractive deep neural networks via learnable light shortcuts. Opt Lett. 2020; 45(10):2688–91. [130] Rahman MSS, Li J, Mengu D, Rivenson Y, Ozcan A. Ensemble learning of diffractive optical networks. Light: Sci Appl. 2021; 10(1):1–13. [131] Muminov B, Vuong LT. Fourier optical preprocessing in lieu of deep learning. Optica. 2020; 7(9):1079–88. [132] Lu L, Zhu L, Zhang Q, Zhu B, Yao Q, Yu M, Niu H, Dong M, Zhong G, Zeng Z. Miniaturized diffraction grating design and processing for deep neural network. IEEE Photon Technol Lett. 2019; 31(24):1952–55. [133] Wu Z, Zhou M, Khoram E, Liu B, Yu Z. Neuromorphic metasurface. Photon Res. 2020; 8(1):46–50. [134] Wang H, Piestun R. Azimuthal multiplexing 3d diffractive optics. Sci Rep. 2020; 10(1):1–9. [135] Huang Z, Wang P, Liu J, Xiong W, He Y, Xiao J, Ye H, Li Y, Chen S, Fan D. All-optical signal processing of vortex beams with diffractive deep neural networks. Phys Rev Appl. 2021; 15(1):014037. [136] Ren H, Shao W, Li Y, Salim F, Gu M. Three-dimensional vectorial holography based on machine learning inverse design. Sci Adv. 2020; 6(16):4261. [137] Sitawarin C, Jin W, Lin Z, Rodriguez AW. Inverse-designed photonic fibers and metasurfaces for nonlinear frequency conversion. Photon Res. 2018; 6(5):82–89. [138] Wiecha PR, Lecestre A, Mallet N, Larrieu G. Pushing the limits of optical information storage using deep learning. Nat Nanotechnol. 2019; 14(3):237–44. [139] Li L, Shuang Y, Ma Q, Li H, Zhao H, Wei M, Liu C, Hao C, Qiu C-W, Cui TJ. Intelligent metasurface imager and recognizer. Light: Sci Appl. 2019; 8(1):1–9. [140] Piggott AY, Ma EY, Su L, Ahn GH, Sapra NV, Vercruysse D, Netherton AM, Khope AS, Bowers JE, Vuckovic J. Inverse-designed photonics for semiconductor foundries. ACS Photon. 2020; 7(3):569–75. [141] Xie Z, Lei T, Qiu H, Zhang Z, Wang H, Yuan X. Broadband on-chip photonic spin hall element via inverse design. Photon Res. 2020; 8(2):121–26. [142] Minkov M, Williamson IA, Andreani LC, Gerace D, Lou B, Song AY, Hughes TW, Fan S. Inverse design of photonic crystals through automatic differentiation. ACS Photon. 2020; 7(7):1729–41. [143] Yang KY, Skarda J, Cotrufo M, Dutt A, Ahn GH, Sawaby M, Vercruysse D, Arbabian A, Fan S, Alù A, et al.Inverse-designed non-reciprocal pulse router for chip-based lidar. Nat Photon. 2020; 14(6):369–74. [144] Chakravarthi S, Chao P, Pederson C, Molesky S, Ivanov A, Hestroffer K, Hatami F, Rodriguez AW, Fu K-MC. Inverse-designed photon extractors for optically addressable defect qubits. Optica. 2020; 7(12):1805–11. [145] Christiansen RE, Wang F, Sigmund O. Topological insulators by topology optimization. Phys Rev Lett. 2019; 122(23):234502. [146] Sapra NV, Yang KY, Vercruysse D, Leedle KJ, Black DS, England RJ, Su L, Trivedi R, Miao Y, Solgaard O, et al.On-chip integrated laser-driven particle accelerator. Science. 2020; 367(6473):79–83. [147] Zhao R, Huang L, Wang Y. Recent advances in multi-dimensional metasurfaces holographic technologies. PhotoniX. 2020; 1(1):1–24. [148] Liu J, Ma Y. A survey of manufacturing oriented topology optimization methods. Adv Eng Softw. 2016; 100:161–75. [149] Zhan A, Gibson R, Whitehead J, Smith E, Hendrickson JR, Majumdar A. Controlling three-dimensional optical fields via inverse mie scattering. Sci Adv. 2019; 5(10):4769. [150] Augenstein Y, Rockstuhl C. Inverse design of nanophotonic devices with structural integrity. ACS Photon. 2020; 7(8):2190–96. [151] Lin Z, Roques-Carmes C, Christiansen RE, Soljačić M, Johnson SG. Computational inverse design for ultra-compact single-piece metalenses free of chromatic and angular aberration. Appl Phys Lett. 2021; 118(4):041104. [152] Zheng B, Yang J, Liang B, Cheng J-c. Inverse design of acoustic metamaterials based on machine learning using a gauss–bayesian model. J Appl Phys. 2020; 128(13):134902. [153] Kudyshev ZA, Kildishev AV, Shalaev VM, Boltasseva A. Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization. Appl Phys Rev. 2020; 7(2):021407. [154] Qu Y, Jing L, Shen Y, Qiu M, Soljacic M. Migrating knowledge between physical scenarios based on artificial neural networks. ACS Photon. 2019; 6(5):1168–74. [155] Yeung C, Tsai J-M, King B, Kawagoe Y, Ho D, Knight MW, Raman AP. Elucidating the behavior of nanophotonic structures through explainable machine learning algorithms. ACS Photon. 2020; 7(8):2309–18. [156] Kiarashinejad Y, Zandehshahvar M, Abdollahramezani S, Hemmatyar O, Pourabolghasem R, Adibi A. Knowledge discovery in nanophotonics using geometric deep learning. Adv Intell Syst. 2020; 2(2):1900132. [157] Wiecha PR, Arbouet A, Girard C, Muskens OL. Deep learning in nano-photonics: inverse design and beyond. arXiv preprint arXiv:2011.12603. 2020. [158] Jiang J, Lupoiu R, Wang EW, Sell D, Hugonin JP, Lalanne P, Fan JA. Metanet: a new paradigm for data sharing in photonics research. Opt Express. 2020; 28(9):13670–81. [159] Goi E, Zhang Q, Chen X, Luan H, Gu M. Perspective on photonic memristive neuromorphic computing. PhotoniX. 2020; 1(1):1–26. [160] Lugnan A, Katumba A, Laporte F, Freiberger M, Sackesyn S, Ma C, Gooskens E, Dambre J, Bienstman P. Photonic neuromorphic information processing and reservoir computing. APL Photon. 2020; 5(2):020901. [161] Ballarini D, Gianfrate A, Panico R, Opala A, Ghosh S, Dominici L, Ardizzone V, De Giorgi M, Lerario G, Gigli G, et al. Polaritonic neuromorphic computing outperforms linear classifiers. Nano Lett. 2020; 20(5):3506–12. [162] de Lima TF, Tait AN, Mehrabian A, Nahmias MA, Huang C, Peng H-T, Marquez BA, Miscuglio M, El-Ghazawi T, Sorger VJ, et al.Primer on silicon neuromorphic photonic processors: architecture and compiler. Nanophotonics. 2020; 9(13):4055–73. [163] Abdollahramezani S, Hemmatyar O, Adibi A. Meta-optics for spatial optical analog computing. Nanophotonics. 2020; 9(13):4075–95. [164] Norman JC, Jung D, Wan Y, Bowers JE. Perspective: The future of quantum dot photonic integrated circuits. APL Photon. 2018; 3(3):030901. [165] Peng H-T, Nahmias MA, De Lima TF, Tait AN, Shastri BJ. Neuromorphic photonic integrated circuits. IEEE J Sel Top Quantum Electron. 2018; 24(6):1–15. [166] Zhang XG, Jiang WX, Jiang HL, Wang Q, Tian HW, Bai L, Luo ZJ, Sun S, Luo Y, Qiu C-W, et al.An optically driven digital metasurface for programming electromagnetic functions. Nat Electron. 2020; 3(3):165–71. [167] Tsilipakos O, Tasolamprou AC, Pitilakis A, Liu F, Wang X, Mirmoosa MS, Tzarouchis DC, Abadal S, Taghvaee H, Liaskos C, et al.Toward intelligent metasurfaces: The progress from globally tunable metasurfaces to software-defined metasurfaces with an embedded network of controllers. Adv Opt Mater. 2020; 8(17):2000783. [168] Ma Q, Cui TJ. Information metamaterials: bridging the physical world and digital world. PhotoniX. 2020; 1(1):1–32. [169] Xu X, Tan M, Corcoran B, Wu J, Boes A, Nguyen TG, Chu ST, Little BE, Hicks DG, Morandotti R, et al.11 tops photonic convolutional accelerator for optical neural networks. Nature. 2021; 589(7840):44–51. [170] Camacho M, Edwards B, Engheta N. A single inverse-designed photonic structure that performs parallel computing. arXiv preprint arXiv:2009.01187. 2020. [171] Zhou T, Lin X, Wu J, Chen Y, Xie H, Li Y, Fan J, Wu H, Fang L, Dai Q. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat Photon. 2021:1–7. [172] Zuo Y, Li B, Zhao Y, Jiang Y, Chen Y-C, Chen P, Jo G-B, Liu J, Du S. All-optical neural network with nonlinear activation functions. Optica. 2019; 6(9):1132–37. -
下载:
点击查看大图
图(1)
计量
- 文章访问数: 170
- HTML全文浏览量: 3
- PDF下载量: 120
- 被引次数: 0
