Citation: | Hu GW, Rho J. Special issue on digital and intelligent optics. Opto-Electron Sci 2, 230050 (2023). doi: 10.29026/oes.2023.230050 |
[1] | Miller DAB. Self-configuring universal linear optical component. Photonics Res 1, 1–15 (2013). doi: 10.1364/PRJ.1.000001 |
[2] | Shen YS, Harris NC, Skirlo S, Prabhu M, Baehr-Jones T et al. Deep learning with coherent nanophotonic circuits. Nat Photonics 11, 441–446 (2017). doi: 10.1038/nphoton.2017.93 |
[3] | Miller DAB. Analyzing and generating multimode optical fields using self-configuring networks. Optica 7, 794–801 (2020). doi: 10.1364/OPTICA.391592 |
[4] | Wang XY, Xie P, Chen BH, Zhang XC. Chip-based high-dimensional optical neural network. Nano-Micro Lett 14, 221 (2022). doi: 10.1007/s40820-022-00957-8 |
[5] | Pai SN, Sun ZH, Hughes TW, Park T, Bartlett B et al. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science 380, 398–404 (2023). doi: 10.1126/science.ade8450 |
[6] | Huang CR, Fujisawa S, de Lima TF, Tait AN, Blow EC et al. A silicon photonic–electronic neural network for fibre nonlinearity compensation. Nat Electron 4, 837–844 (2021). doi: 10.1038/s41928-021-00661-2 |
[7] | Filipovich MJ, Guo ZM, Al-Qadasi M, Marquez BA, Morison HD et al. Silicon photonic architecture for training deep neural networks with direct feedback alignment. Optica 9, 1323–1332 (2022). doi: 10.1364/OPTICA.475493 |
[8] | Tait AN, de Lima TF, Zhou E, Wu AX, Nahmias MA et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci Rep 7, 7430 (2017). doi: 10.1038/s41598-017-07754-z |
[9] | Shastri BJ, Tait AN, de Lima TF, Pernice WHP, Bhaskaran H et al. Photonics for artificial intelligence and neuromorphic computing. Nat Photonics 15, 102–114 (2021). doi: 10.1038/s41566-020-00754-y |
[10] | Feldmann J, Youngblood N, Karpov M, Gehring H, Li X et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589, 52–58 (2021). doi: 10.1038/s41586-020-03070-1 |
[11] | Han YN, Xiang SY, Song ZW, Gao S, Guo XX et al. Pattern recognition in multi-synaptic photonic spiking neural networks based on a DFB-SA chip. Opto-Electron Sci 2, 230021 (2023). doi: 10.29026/oes.2023.230021 |
[12] | Chen ZJ, Sludds A, Davis III R, Christen I, Bernstein L et al. Deep learning with coherent VCSEL neural networks. Nat Photonics 17, 723–730 (2023). doi: 10.1038/s41566-023-01233-w |
[13] | Xu XY, Tan MX, Corcoran B, Wu JY, Boes A et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589, 44–51 (2021). doi: 10.1038/s41586-020-03063-0 |
[14] | Mennel L, Symonowicz J, Wachter S, Polyushkin DK, Molina-Mendoza AJ et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020). doi: 10.1038/s41586-020-2038-x |
[15] | Krasikov S, Tranter A, Bogdanov A, Kivshar Y. Intelligent metaphotonics empowered by machine learning. Opto-Electron Adv 5, 210147 (2022). doi: 10.29026/oea.2022.210147 |
[16] | Zhao BH, Cheng JW, Wu B, Gao DS, Zhou HL et al. Integrated photonic convolution acceleration core for wearable devices. Opto-Electron Sci 2, 230017 (2023). doi: 10.29026/oes.2023.230017 |
[17] | Wang XY, Qiu XK, Liu ML, Liu F, Li MM et al. Flat soliton microcomb source. Opto-Electron Sci 2, 230024 (2023). doi: 10.29026/oes.2023.230024 |
[18] | Zhang M, Buscaino B, Wang C, Shams-Ansari A, Reimer C et al. Broadband electro-optic frequency comb generation in a lithium niobate microring resonator. Nature 568, 373–377 (2019). doi: 10.1038/s41586-019-1008-7 |
[19] | Diddams SA, Vahala K, Udem T. Optical frequency combs: coherently uniting the electromagnetic spectrum. Science 369, eaay3676 (2020). doi: 10.1126/science.aay3676 |
[20] | Lucas E, Yu SP, Briles TC, Carlson DR, Papp SB. Tailoring microcombs with inverse-designed, meta-dispersion microresonators. Nat Photonics 17, 943–950 (2023). doi: 10.1038/s41566-023-01252-7 |
[21] | Buddhiraju S, Dutt A, Minkov M, Williamson IAD, Fan SH. Arbitrary linear transformations for photons in the frequency synthetic dimension. Nat Commun 12, 2401 (2021). doi: 10.1038/s41467-021-22670-7 |
[22] | Lyu JB, Zhu T, Zhou Y, Chen ZM, Pi YZ et al. Inverse design for material anisotropy and its application for a compact X-cut TFLN on-chip wavelength demultiplexer. Opto-Electron Sci 2, 230038 (2023). doi: 10.29026/oes.2023.230038 |
[23] | Xiao YT, Chen LW, Pu MB, Xu MF, Zhang Q et al. Improved spatiotemporal resolution of anti-scattering super-resolution label-free microscopy via synthetic wave 3D metalens imaging. Opto-Electron Sci 2, 230037 (2023). doi: 10.29026/oes.2023.230037 |
[24] | Su DE, Li XY, Gao WD, Wei QH, Li HY et al. Smart Palm-size Optofluidic Hematology Analyzer for automated imaging-based leukocyte concentration detection. Opto-Electron Sci 2, 230018 (2023). doi: 10.29026/oes.2023.230018 |
[25] | Wang XW, Wang H, Wang JL, Liu XS, Hao HJ et al. Single-shot isotropic differential interference contrast microscopy. Nat Commun 14, 2063 (2023). doi: 10.1038/s41467-023-37606-6 |
[26] | Zhao WS, Zhao SQ, Han ZQ, Ding XY, Hu GW et al. Enhanced detection of fluorescence fluctuations for high-throughput super-resolution imaging. Nat Photonics 17, 806–813 (2023). doi: 10.1038/s41566-023-01234-9 |
TOC