[1] Shastri B J, Tait A N, Lima T F, et al. Photonics for artificial intelligence and neuromorphic computing [J]. Nature Photonics, 2021, 15: 102-114. doi:  10.1038/s41566-020-00754-y
[2] Bogaerts W, Perez D, Capmany J, et al. Programmable photonic circuits [J]. Nature, 2020, 586: 207-216. doi:  10.1038/s41586-020-2764-0
[3] Xu S, Zou X, Ma B, et al. Deep-learning-powered photonic analog-to-digital conversion [J]. Light: Science & Applications, 2019, 8: 66.
[4] Shen Y, Harris N, Skirlo S, et al. Deep learning with coherent nanophotonic circuits [J]. Nature Photonics, 2017, 11: 441-447. doi:  10.1038/nphoton.2017.93
[5] Feldmann J, Youngblood N, Wright C, et al. All-optical spiking neurosynaptic networks with self-learning capabilities [J]. Nature, 2019, 569: 208-214. doi:  10.1038/s41586-019-1157-8
[6] Fard M, Williamson I, Edwards M, et al. Experimental realization of arbitrary activation functions for optical neural networks [J]. Optics Express, 2020, 28: 12138-12148. doi:  10.1364/OE.391473
[7] Zhou T, Lin X, Wu J, et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit [J]. Nature Photonics, 2021, 15: 367-373. doi:  10.1038/s41566-021-00796-w
[8] Atabaki A, Moazeni S, Pavanello F, et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip [J]. Nature, 2018, 556: 349-354. doi:  10.1038/s41586-018-0028-z
[9] Wang Z, Tian B, Pantouvaki M, et al. Room-temperature InP distributed feedback laser array directly grown on silicon [J]. Nature Photonics, 2015, 9: 837-842. doi:  10.1038/nphoton.2015.199