The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Microcomb-driven silicon photonic systems
Open accessAbstract
Microcombs have sparked a surge of applications over the past decade, ranging from optical communications to metrology 1–4. Despite their diverse deployment, most microcomb-based systems rely on a large amount of bulky elements and equipment to fulfil their desired functions, which is complicated, expensive and power consuming. By contrast, foundry-based silicon photonics (SiPh) has had remarkable success in providing versatile functionality in a scalable and low-cost manner 5–7, but its available chip-based light sources lack the capacity for parallelization, which limits the scope of SiPh applications. Here we combine these two technologies by using a power-efficient and operationally simple aluminium-gallium-arsenide-on-insulator microcomb source to drive complementary metal–oxide–semiconductor SiPh engines. We present two important chip-scale photonic systems for optical data transmission and microwave photonics, respectively. A microcomb-based integrated photonic data link is demonstrated, based on a pulse-amplitude four-level modulation scheme with a two-terabit-per-second aggregate rate, and a highly reconfigurable microwave photonic filter with a high level of integration is constructed using a time-stretch approach. Such synergy of a microcomb and SiPh integrated components is an essential step towards the next generation of fully integrated photonic systems.
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Cites
Deep learning with coherent nanophotonic circuits
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Parallel convolutional processing using an integrated photonic tensor core
Feldmann, J., Youngblood, N., Karpov, M., Gehring, H., Li, X., Stappers, M., Le Gallo, M., Fu, X., Lukashchuk, A., Raja, A. S., et al. (2021) · Nature · doi:10.1038/s41586-020-03070-1 · Cited by 1,900
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Integrated photonic quantum technologies
Wang, J., Sciarrino, F., Laing, A., & Thompson, M. G. (2020) · Nature Photonics · doi:10.1038/s41566-019-0532-1 · Cited by 1,576
All-optical spiking neurosynaptic networks with self-learning capabilities
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11 TOPS photonic convolutional accelerator for optical neural networks
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Microresonator-based solitons for massively parallel coherent optical communications
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Integrated microwave photonics
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Cited by
Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence
Xu, Z., Zhou, T., Ma, M., Deng, C., Dai, Q., & Fang, L. (2024) · Science · doi:10.1126/science.adl1203 · Cited by 308
Single chip photonic deep neural network with accelerated training
Bandyopadhyay, S., Sludds, A., Krastanov, S., Hamerly, R., Harris, N., Bunandar, D., Streshinsky, M., Hochberg, M., & Englund, D. (2022) · arXiv · doi:10.48550/arxiv.2208.01623 · Cited by 265
Reynaud, M., Waqar, M., Du, C., Posadas, A. B., Pan, X., & Demkov, A. A. (2025) · Communications Materials · doi:10.1038/s43246-025-00908-x · Cited by 14
Metadata
- Authors
- Shu, H., Chang, L., Tao, Y., Shen, B., Xie, W., Jin, M., Netherton, A., Tao, Z., Zhang, X., Chen, R., Bai, B., Qin, J., Yu, S., Wang, X., & Bowers, J. E.
- Venue
- Nature
- Published
- 2022-05-18
- Volume
- 605
- Issue
- 7910
- Pages
- 457-463
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4280617936
- Cited by
- 437
- References
- 84
- FWCI
- 25.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Shu, H., Chang, L., Tao, Y., Shen, B., Xie, W., Jin, M., Netherton, A., Tao, Z., Zhang, X., Chen, R., Bai, B., Qin, J., Yu, S., Wang, X., & Bowers, J. E. (2022). Microcomb-driven silicon photonic systems. Nature. https://doi.org/10.1038/s41586-022-04579-3
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