Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks

Yang Shi, Junyu Ren, Guanyu Chen, Wei Liu, Chuqi Jin, Xiangyu Guo, Yu Yu () and Xinliang Zhang
Additional contact information
Yang Shi: Huazhong University of Science and Technology
Junyu Ren: Huazhong University of Science and Technology
Guanyu Chen: Huazhong University of Science and Technology
Wei Liu: Huazhong University of Science and Technology
Chuqi Jin: Huazhong University of Science and Technology
Xiangyu Guo: Huazhong University of Science and Technology
Yu Yu: Huazhong University of Science and Technology
Xinliang Zhang: Huazhong University of Science and Technology

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Silicon photonics is promising for artificial neural networks computing owing to its superior interconnect bandwidth, low energy consumption and scalable fabrication. However, the lack of silicon-integrated and monitorable optical neurons limits its revolution in large-scale artificial neural networks. Here, we highlight nonlinear germanium-silicon photodiodes to construct on-chip optical neurons and a self-monitored all-optical neural network. With specifically engineered optical-to-optical and optical-to-electrical responses, the proposed neuron merges the all-optical activation and non-intrusive monitoring functions in a compact footprint of 4.3 × 8 μm2. Experimentally, a scalable three-layer photonic neural network enables in situ training and learning in object classification and semantic segmentation tasks. The performance of this neuron implemented in a deep-scale neural network is further confirmed via handwriting recognition, achieving a high accuracy of 97.3%. We believe this work will enable future large-scale photonic intelligent processors with more functionalities but simplified architecture.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-022-33877-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33877-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-33877-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().