[1] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner. Gradient-based learning applied to document recognition. Proc. IEEE, 86, 2278-2324(1998).

[2] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015).

[3] J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, T. Chen. Recent advances in convolutional neural networks. Pattern Recogn., 77, 354-377(2018).

[4] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks. Commun. ACM, 60, 84-90(2017).

[5] A. Voulodimos, N. Doulamis, A. Doulamis, E. Protopapadakis. Deep learning for computer vision: a brief review. Comput. Intell. Neurosci., 2018, 7068349(2018).

[6] D. A. Forsyth, J. Ponce. Computer Vision: A Modern Approach(2002).

[7] M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, K. Zieba. End to end learning for self-driving cars. arXiv(2016).

[8] J. Levinson, J. Askeland, J. Becker, J. Dolson, D. Held, S. Kammel, J. Z. Kolter, D. Langer, O. Pink, V. Pratt, M. Sokolsky, G. Stanek, D. Stavens, A. Teichman, M. Werling, S. Thrun. Towards fully autonomous driving: systems and algorithms. IEEE Intelligent Vehicles Symposium (IV), 163-168(2011).

[9] S. Grigorescu, B. Trasnea, T. Cocias, G. Macesanu. A survey of deep learning techniques for autonomous driving. J. Field Robot., 37, 362-386(2020).

[10] J. Hirschberg, C. D. Manning. Advances in natural language processing. Science, 349, 261-266(2015).

[11] P. M. Nadkarni, L. Ohno-Machado, W. W. Chapman. Natural language processing: an introduction. J. Am. Med. Inf. Assoc., 18, 544-551(2011).

[12] K. Chowdhary. Natural language processing. Fundamentals of Artificial Intelligence, 603-649(2020).

[13] D. Shen, G. Wu, H.-I. Suk. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng., 19, 221(2017).

[14] E. Gawehn, J. A. Hiss, G. Schneider. Deep learning in drug discovery. Mol. Inf., 35, 3-14(2016).

[15] C. Angermueller, T. Pärnamaa, L. Parts, O. Stegle. Deep learning for computational biology. Mol. Syst. Biol., 12, 878(2016).

[16] J. L. Hennessy, D. A. Patterson. Computer Architecture: A Quantitative Approach(2011).

[17] D. Kirk. NVIDIA CUDA software and GPU parallel computing architecture. 6th International Symposium on Memory Management, 103-104(2007).

[18] N. P. Jouppi. In-datacenter performance analysis of a tensor processing unit. Proceedings of the 44th Annual International Symposium on Computer Architecture, 1-12(2017).

[19] C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, J. Cong. Optimizing FPGA-based accelerator design for deep convolutional neural networks. Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 161-170(2015).

[20] H. J. Caulfield, S. Dolev. Why future supercomputing requires optics. Nat. Photonics, 4, 261-263(2010).

[21] D. A. Miller. The role of optics in computing. Nat. Photonics, 4, 406(2010).

[22] J. Touch, A.-H. Badawy, V. J. Sorger. Optical computing. Nanophotonics, 6, 503-505(2017).

[23] G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, L. Zdeborová. Machine learning and the physical sciences. Rev. Mod. Phys., 91, 045002(2019).

[24] B. J. Shastri, A. N. Tait, T. Ferreira de Lima, W. H. Pernice, H. Bhaskaran, C. D. Wright, P. R. Prucnal. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics, 15, 102-114(2021).

[25] W. Bogaerts, D. Pérez, J. Capmany, D. A. Miller, J. Poon, D. Englund, F. Morichetti, A. Melloni. Programmable photonic circuits. Nature, 586, 207-216(2020).

[26] D. Marković, A. Mizrahi, D. Querlioz, J. Grollier. Physics for neuromorphic computing. Nat. Rev. Phys., 2, 499-510(2020).

[27] H. Zhou, J. Dong, J. Cheng, W. Dong, C. Huang, Y. Shen, Q. Zhang, M. Gu, C. Qian, H. Chen, Z. Ruan, X. Zhang. Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl., 11, 30(2022).

[28] Q. Cheng, M. Bahadori, M. Glick, S. Rumley, K. Bergman. Recent advances in optical technologies for data centers: a review. Optica, 5, 1354-1370(2018).

[29] J. Yao. Microwave photonics. J. Lightwave Technol., 27, 314-335(2009).

[30] Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, M. Soljačić. Deep learning with coherent nanophotonic circuits. Nat. Photonics, 11, 441-446(2017).

[31] G. Wetzstein, A. Ozcan, S. Gigan, S. Fan, D. Englund, M. Soljačić, C. Denz, D. A. Miller, D. Psaltis. Inference in artificial intelligence with deep optics and photonics. Nature, 588, 39-47(2020).

[32] H. Zhang, M. Gu, X. Jiang, J. Thompson, H. Cai, S. Paesani, R. Santagati, A. Laing, Y. Zhang, M. Yung, Y. Z. Shi, F. K. Muhammad, G. Q. Lo, X. S. Luo, B. Dong, D. L. Kwong, L. C. Kwek, A. Q. Liu. An optical neural chip for implementing complex-valued neural network. Nat. Commun., 12, 457(2021).

[33] A. N. Tait, T. F. De Lima, E. Zhou, A. X. Wu, M. A. Nahmias, B. J. Shastri, P. R. Prucnal. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep., 7, 7430(2017).

[34] A. N. Tait, M. A. Nahmias, B. J. Shastri, P. R. Prucnal. Broadcast and weight: an integrated network for scalable photonic spike processing. J. Lightwave Technol., 32, 4029-4041(2014).

[35] C. Huang, S. Fujisawa, T. F. de Lima, A. N. Tait, E. C. Blow, Y. Tian, S. Bilodeau, A. Jha, F. Yaman, H.-T. Peng, H. G. Batshon, B. J. Shastri, Y. Inada, T. Wang, P. R. Prucnal. A silicon photonic–electronic neural network for fibre nonlinearity compensation. Nat. Electron., 4, 837-844(2021).

[36] J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A. S. Raja, J. Liu, C. D. Wright, A. Sebastian, T. J. Kippenberg, W. H. P. Pernice, H. Bhaskaran. Parallel convolutional processing using an integrated photonic tensor core. Nature, 589, 52-58(2021).

[37] C. Wu, H. Yu, S. Lee, R. Peng, I. Takeuchi, M. Li. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nat. Commun., 12, 96(2021).

[38] Y. Huang, W. Zhang, F. Yang, J. Du, Z. He. Programmable matrix operation with reconfigurable time-wavelength plane manipulation and dispersed time delay. Opt. Express, 27, 20456-20467(2019).

[39] X. Xu, M. Tan, B. Corcoran, J. Wu, T. G. Nguyen, A. Boes, S. T. Chu, B. E. Little, R. Morandotti, A. Mitchell, D. G. Hicks, D. J. Moss. Photonic perceptron based on a Kerr microcomb for high-speed, scalable, optical neural networks. Laser Photon. Rev., 14, 2000070(2020).

[40] X. Xu, M. Tan, B. Corcoran, J. Wu, A. Boes, T. G. Nguyen, S. T. Chu, B. E. Little, D. G. Hicks, R. Morandotti, A. Mitchell, D. J. Moss. 11 tops photonic convolutional accelerator for optical neural networks. Nature, 589, 44-51(2021).

[41] X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, A. Ozcan. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

[42] T. Zhou, X. Lin, J. Wu, Y. Chen, H. Xie, Y. Li, J. Fan, H. Wu, L. Fang, Q. Dai. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics, 15, 367-373(2021).

[43] Z. Xu, X. Yuan, T. Zhou, L. Fang. A multichannel optical computing architecture for advanced machine vision. Light Sci. Appl., 11, 255(2022).

[44] T. Yan, R. Yang, Z. Zheng, X. Lin, H. Xiong, Q. Dai. All-optical graph representation learning using integrated diffractive photonic computing units. Sci. Adv., 8, eabn7630(2022).

[45] H. Zhu, J. Zou, H. Zhang, Y. Shi, S. Luo, N. Wang, H. Cai, L. Wan, B. Wang, X. Jiang, J. Thompson, X. S. Luo, X. H. Zhou, L. M. Xiao, W. Huang, L. Patrick, M. Gu, L. C. Kwek, A. Q. Liu. Space-efficient optical computing with an integrated chip diffractive neural network. Nat. Commun., 13, 1044(2022).

[46] X. Zhao, H. Lv, C. Chen, S. Tang, X. Liu, Q. Qi. On-chip reconfigurable optical neural networks(2021).

[47] Z. Wang, L. Chang, F. Wang, T. Li, T. Gu. Integrated photonic metasystem for image classifications at telecommunication wavelength. Nat. Commun., 13, 2131(2022).

[48] T. Fu, Y. Zang, H. Huang, Z. Du, C. Hu, M. Chen, S. Yang, H. Chen. On-chip photonic diffractive optical neural network based on a spatial domain electromagnetic propagation model. Opt. Express, 29, 31924-31940(2021).

[49] T. Fu, Y. Zang, Y. Huang, Z. Du, H. Huang, C. Hu, M. Chen, S. Yang, H. Chen. Photonic machine learning with on-chip diffractive optics. Nat. Commun., 14, 70(2023).

[50] A. Hirose. Complex-Valued Neural Networks: Theories and Applications, 5(2003).

[51] N. Özdemir, B. B. İskender, N. Y. Özgür. Complex valued neural network with Möbius activation function. Commun. Nonlinear Sci. Numer. Simulation, 16, 4698-4703(2011).

[52] S. Scardapane, S. Van Vaerenbergh, A. Hussain, A. Uncini. Complex-valued neural networks with nonparametric activation functions. IEEE Trans. Emerg. Top. Comput. Intell., 4, 140-150(2018).

[53] X. Ding, X. Zhang, J. Han, G. Ding. Diverse branch block: building a convolution as an inception-like unit. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10886-10895(2021).

[54] X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, J. Sun. Repvgg: making vgg-style convnets great again. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13733-13742(2021).

[55] X. Ding, T. Hao, J. Tan, J. Liu, J. Han, Y. Guo, G. Ding. Resrep: lossless CNN pruning via decoupling remembering and forgetting. Proceedings of the IEEE/CVF International Conference on Computer Vision, 4510-4520(2021).

[56] B. Goyal, A. Dogra, S. Agrawal, B. S. Sohi, A. Sharma. Image denoising review: from classical to state-of-the-art approaches. Inf. Fusion, 55, 220-244(2020).

[57] C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, C.-W. Lin. Deep learning on image denoising: an overview. Neural Netw., 131, 251-275(2020).

[58] K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang. Beyond a Gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Process., 26, 3142-3155(2017).

[59] Y. Chen, T. Pock. Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell., 39, 1256-1272(2016).

[60] A. Rahim, A. Hermans, B. Wohlfeil, D. Petousi, B. Kuyken, D. Van Thourhout, R. G. Baets. Taking silicon photonics modulators to a higher performance level: state-of-the-art and a review of new technologies. Adv. Photon., 3, 024003(2021).

[61] A. Samani, M. Chagnon, D. Patel, V. Veerasubramanian, S. Ghosh, M. Osman, Q. Zhong, D. V. Plant. A low-voltage 35-GHz silicon photonic modulator-enabled 112-Gb/s transmission system. IEEE Photon. J., 7, 7901413(2015).

[62] T. Baehr-Jones, R. Ding, Y. Liu, A. Ayazi, T. Pinguet, N. C. Harris, M. Streshinsky, P. Lee, Y. Zhang, A. E.-J. Lim, T.-Y. Liow, S. H.-G. Teo, G.-Q. Lo, M. Hochberg. Ultralow drive voltage silicon traveling-wave modulator. Opt. Express, 20, 12014-12020(2012).

[63] M. Sakib, P. Liao, C. Ma, R. Kumar, D. Huang, G.-L. Su, X. Wu, S. Fathololoumi, H. Rong. A high-speed micro-ring modulator for next generation energy-efficient optical networks beyond 100 Gbaud. CLEO: Science and Innovations, SF1C–3(2021).

[64] C. Liu, J. Guo, L. Yu, J. Li, M. Zhang, H. Li, Y. Shi, D. Dai. Silicon/2D-material photodetectors: from near-infrared to mid-infrared. Light Sci. Appl., 10, 1(2021).

[65] Y.-Q. Bie, G. Grosso, M. Heuck, M. M. Furchi, Y. Cao, J. Zheng, D. Bunandar, E. Navarro-Moratalla, L. Zhou, D. K. Efetov, T. Taniguchi, K. Watanabe, J. Kong, D. Englund, P. Jarillo-Herrero. A MoTe₂-based light-emitting diode and photodetector for silicon photonic integrated circuits. Nat. Nanotechnol., 12, 1124-1129(2017).

[66] S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, E. Shelhamer. CUDNN: efficient primitives for deep learning. arXiv(2014).

[67] J. Choquette, W. Gandhi, O. Giroux, N. Stam, R. Krashinsky. NVIDIA A100 tensor core GPU: performance and innovation. IEEE Micro, 41, 29-35(2021).

[68] H. Liao, J. Tu, J. Xia, X. Zhou. Davinci: a scalable architecture for neural network computing. Hot Chips Symposium, 1-44(2019).

[69] N. Jouppi, C. Young, N. Patil, D. Patterson. Motivation for and evaluation of the first tensor processing unit. IEEE Micro, 38, 10-19(2018).

[70] P. Yao, H. Wu, B. Gao, J. Tang, Q. Zhang, W. Zhang, J. J. Yang, H. Qian. Fully hardware-implemented memristor convolutional neural network. Nature, 577, 641-646(2020).

[71] Y. Li, J. Dongarra, S. Tomov. A note on auto-tuning GEMM for GPUs. Computational Science–ICCS 2009: 9th International Conference, 884-892(2009).

[72] R. Nath, S. Tomov, J. Dongarra. An improved magma gemm for fermi graphics processing units. Int. J. High Performance Comput. Appl., 24, 511-515(2010).

[73] D. Yan, W. Wang, X. Chu. Demystifying tensor cores to optimize half-precision matrix multiply. IEEE International Parallel and Distributed Processing Symposium (IPDPS), 634-643(2020).

[74] S. Xu, J. Wang, S. Yi, W. Zou. High-order tensor flow processing using integrated photonic circuits. Nat. Commun., 13, 7970(2022).

[75] V. Bangari, B. A. Marquez, H. Miller, A. N. Tait, M. A. Nahmias, T. F. De Lima, H.-T. Peng, P. R. Prucnal, B. J. Shastri. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNS). IEEE J. Sel. Top. Quantum Electron., 26, 7701213(2019).

[76] S. Xu, J. Wang, H. Shu, Z. Zhang, S. Yi, B. Bai, X. Wang, J. Liu, W. Zou. Optical coherent dot-product chip for sophisticated deep learning regression. Light Sci. Appl., 10, 221(2021).

[77] B. Bai, Q. Yang, H. Shu, L. Chang, F. Yang, B. Shen, Z. Tao, J. Wang, S. Xu, W. Xie, W. Zou, W. Hu, J. E. Bowers, X. Wang. Microcomb-based integrated photonic processing unit. Nat. Commun., 14, 66(2023).

[78] W. M. Green, M. J. Rooks, L. Sekaric, Y. A. Vlasov. Ultra-compact, low RF power, 10 Gb/s silicon Mach-Zehnder modulator. Opt. Express, 15, 17106-17113(2007).

[79] C. Sun, M. T. Wade, Y. Lee, J. S. Orcutt, L. Alloatti, M. S. Georgas, A. S. Waterman, J. M. Shainline, R. R. Avizienis, S. Lin, B. R. Moss, R. Kumar, F. Pavanello, A. H. Atabaki, H. M. Cook, A. J. Ou, J. C. Leu, Y.-H. Chen, K. Asanović, R. J. Ram, M. A. Popović, V. M. Stojanović. Single-chip microprocessor that communicates directly using light. Nature, 528, 534-538(2015).

[80] Y. LeCun. 1.1 deep learning hardware: past, present, and future. IEEE International Solid-State Circuits Conference (ISSCC), 12-19(2019).

[81] K. He, X. Zhang, S. Ren, J. Sun. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778(2016).

[82] K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv(2014).

[83] R. R. Schaller. Moore’s law: past, present and future. IEEE Spectrum, 34, 52-59(1997).

[84] W. Shi, J. Cao, Q. Zhang, Y. Li, L. Xu. Edge computing: vision and challenges. IEEE Internet Things J., 3, 637-646(2016).

[85] Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief. A survey on mobile edge computing: the communication perspective. Commun. Surveys Tuts., 19, 2322-2358(2017).

[86] M. Satyanarayanan. The emergence of edge computing. Computer, 50, 30-39(2017).

[87] D. Ielmini, H.-S. P. Wong. In-memory computing with resistive switching devices. Nat. Electron., 1, 333-343(2018).

[88] A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, E. Eleftheriou. Memory devices and applications for in-memory computing. Nat. Nanotechnol., 15, 529-544(2020).

[89] N. Verma, H. Jia, H. Valavi, Y. Tang, M. Ozatay, L.-Y. Chen, B. Zhang, P. Deaville. In-memory computing: advances and prospects. IEEE Solid-State Circuits Mag., 11, 43-55(2019).

[90] Y. Chen, G. Yin, Z. Tan, M. Lee, Z. Yang, Y. Liu, H. Yang, K. Ma, X. Li. Yoloc: deploy large-scale neural network by rom-based computing-in-memory using residual branch on a chip. arXiv(2022).

[91] B. Mukherjee. WDM optical communication networks: progress and challenges. IEEE J. Sel. Areas Commun., 18, 1810-1824(2000).

[92] B. B. Buckley, S. T. Fryslie, K. Guinn, G. Morrison, A. Gazman, Y. Shen, K. Bergman, M. L. Mashanovitch, L. A. Johansson. WDM source based on high-power, efficient 1280-nm DFB lasers for terabit interconnect technologies. IEEE Photon. Technol. Lett., 30, 1929-1932(2018).

[93] L. Chang, S. Liu, J. E. Bowers. Integrated optical frequency comb technologies. Nat. Photonics, 16, 95-108(2022).

[94] T. J. Kippenberg, R. Holzwarth, S. A. Diddams. Microresonator-based optical frequency combs. Science, 332, 555-559(2011).

[95] Y. K. Chembo. Kerr optical frequency combs: theory, applications and perspectives. Nanophotonics, 5, 214-230(2016).

[96] Y. Shoji, K. Kintaka, S. Suda, H. Kawashima, T. Hasama, H. Ishikawa. Low-crosstalk 2 × 2 thermo-optic switch with silicon wire waveguides. Opt. Express, 18, 9071-9075(2010).

[97] L. Lu, S. Zhao, L. Zhou, D. Li, Z. Li, M. Wang, X. Li, J. Chen. 16 × 16 non-blocking silicon optical switch based on electro-optic Mach-Zehnder interferometers. Opt. Express, 24, 9295-9307(2016).

[98] L. Qiao, W. Tang, T. Chu. 32 × 32 silicon electro-optic switch with built-in monitors and balanced-status units. Sci. Rep., 7, 42306(2017).

[99] P. Dong, S. F. Preble, M. Lipson. All-optical compact silicon comb switch. Opt. Express, 15, 9600-9605(2007).

[100] Y. H. Wen, O. Kuzucu, T. Hou, M. Lipson, A. L. Gaeta. All-optical switching of a single resonance in silicon ring resonators. Opt. Lett., 36, 1413-1415(2011).

[101] B. G. Lee, A. Biberman, P. Dong, M. Lipson, K. Bergman. All-optical comb switch for multiwavelength message routing in silicon photonic networks. IEEE Photon. Technol. Lett., 20, 767-769(2008).

[102] N. Sherwood-Droz, H. Wang, L. Chen, B. G. Lee, A. Biberman, K. Bergman, M. Lipson. Optical 4 × 4 hitless silicon router for optical networks-on-chip (NOC). Opt. Express, 16, 15915-15922(2008).

[103] S. Han, T. J. Seok, K. Yu, N. Quack, R. S. Muller, M. C. Wu. Large-scale polarization-insensitive silicon photonic MEMS switches. J. Lightwave Technol., 36, 1824-1830(2018).

[104] K. Kwon, T. J. Seok, J. Henriksson, J. Luo, L. Ochikubo, J. Jacobs, R. S. Muller, M. C. Wu. 128 × 128 silicon photonic MEMS switch with scalable row/column addressing. CLEO: Science and Innovations, SF1A–4(2018).

[105] H. Y. Hwang, J. S. Lee, T. J. Seok, A. Forencich, H. R. Grant, D. Knutson, N. Quack, S. Han, R. S. Muller, G. C. Papen, M. C. Wu, P. O’Brien. Flip chip packaging of digital silicon photonics MEMS switch for cloud computing and data centre. IEEE Photon. J., 9, 2900210(2017).

[106] L. Liao, D. Samara-Rubio, M. Morse, A. Liu, D. Hodge, D. Rubin, U. D. Keil, T. Franck. High speed silicon Mach-Zehnder modulator. Opt. Express, 13, 3129-3135(2005).

[107] C. Wang, M. Zhang, X. Chen, M. Bertrand, A. Shams-Ansari, S. Chandrasekhar, P. Winzer, M. Lončar. Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages. Nature, 562, 101-104(2018).

[108] Y. Ogiso, J. Ozaki, Y. Ueda, H. Wakita, M. Nagatani, H. Yamazaki, M. Nakamura, T. Kobayashi, S. Kanazawa, Y. Hashizume, H. Tanobe, N. Nunoya, M. Ida, Y. Miyamoto, M. Ishikawa. 80-GHz bandwidth and 1.5-V V_π InP-based IQ modulator. J. Lightwave Technol., 38, 249-255(2019).

[109] Z. Zhao, J. Liu, Y. Liu, N. Zhu. High-speed photodetectors in optical communication system. J. Semicond., 38, 121001(2017).

[110] S. Malyshev, A. Chizh. State of the art high-speed photodetectors for microwave photonics application. 15th International Conference on Microwaves, Radar and Wireless Communications, 765-775(2004).

[111] A. E.-J. Lim, J. Song, Q. Fang, C. Li, X. Tu, N. Duan, K. K. Chen, R. P.-C. Tern, T.-Y. Liow. Review of silicon photonics foundry efforts. IEEE J. Sel. Top. Quantum Electron., 20, 405-416(2013).

[112] S. Y. Siew, B. Li, F. Gao, H. Y. Zheng, W. Zhang, P. Guo, S. W. Xie, A. Song, B. Dong, L. W. Luo, C. Li, X. Luo, G.-Q. Lo. Review of silicon photonics technology and platform development. J. Lightwave Technol., 39, 4374-4389(2021).