A lithography-free photonic processor through dynamic control of optical gain distributions is demonstrated, allowing reconfigurable photonic neural networks and more efficient signal processing, and showing great promise in easing data traffic as well as accelerating information processing speeds.
This is a preview of subscription content, log in via an institution to check access.
References
Xu, X. et al. Nature 589, 44–51 (2021).
Bandyopadhyay, S. et al. Preprint at https://doi.org/10.48550/arXiv.2208.01623 (2022).
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. Nature 569, 208–214 (2019).
Feldmann, J. et al. Nature 589, 52–58 (2021).
Shen, Y. et al. Nat. Photon. 11, 441–446 (2017).
Boes, A., Corcoran, B., Chang, L., Bowers, J. & Mitchell, A. Laser Photon. Rev. 12, 1700256 (2018).
Ovvyan, A. P., Gruhler, N., Ferrari, S. & Pernice, W. H. P. J. Opt. 18, 064011 (2016).
Xu, Q., Schmidt, B., Pradhan, S. & Lipson, M. Nature 435, 325–327 (2005).
Fang, Z. et al. Nat. Nanotechnol. 17, 842–848 (2022).
Capmany, J., Gasulla, I. & Pérez, D. Nat. Photon. 10, 6–8 (2016).
Elshaari, A. W. et al. Nat. Photon. 14, 285–298 (2020).
Shastri, B. J. et al. Nat. Photon. 15, 102–114 (2021).
Wu, T., Menarini, M., Gao, Z. & Feng, L. Nat. Photon. https://doi.org/10.1038/s41566-023-01205-0 (2023).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Ovvyan, A.P., Pernice, W.H.P. Lithography-free reconfigurable photonic processor. Nat. Photon. 17, 644–645 (2023). https://doi.org/10.1038/s41566-023-01253-6
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41566-023-01253-6
- Springer Nature Limited