Thermally Controlled Multi‐Functional Waveguide Photodetector
Abstract Photodetectors are one of the fundamental building blocks in integrated photonic systems. They mainly serve to convert optical to electrical signals by absorbing photons in semiconductors which have a bandgap smaller than the photon energy. The constraint on photon energy in relation to the bandgap of commonly available semiconductor materials hinders the application of integrated photonics for some emerging applications. Here a novel waveguide detector integrated with on‐chip heater is proposed. Tunable bandgap can be achieved via local heating, which changes absorption characteristics. Based on this mechanism, the multi‐functional germanium detector for three different applications including broadband optical communications, optical neural networks, and optical spectral sensing is demonstrated. The proposed photodetector enables high‐speed detection at extended long wavelengths. In an artificial neural network, the controllable photoresponse allows for a tailorable nonlinear activation function to be implemented. It is also capable of retrieving spectral information via a single tunable detector without the need for any other optical components. This work not only proposes a new waveguide photodetector structure but also identify an approach to make multi‐functional photodetectors that can be used in different photonic integration platforms.
Authors
- Ke Xu (ORCID: https://orcid.org/0000-0003-1935-8910)
- Hon Ki Tsang (ORCID: https://orcid.org/0000-0003-2777-1537)
- Guoyi Tao
- Chaoran Huang (ORCID: https://orcid.org/0000-0001-8205-8254)
- Zimeng Zhang (ORCID: https://orcid.org/0000-0002-0973-8891)
- Jianing Wang (ORCID: https://orcid.org/0009-0003-0667-5260)
- Yihang Li (ORCID: https://orcid.org/0000-0002-7060-9036)
- Shumin Xiao (ORCID: https://orcid.org/0000-0001-9888-5174)
- Qinghai Song
Institutions
- Chinese University of Hong Kong (HK)
- Harbin Institute of Technology (CN)
- Peng Cheng Laboratory (CN)
Publication Details
- Journal
- Laser & Photonics Review
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1002/lpor.202402072
- Citations
- 5
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- National Natural Science Foundation of China
- Science, Technology and Innovation Commission of Shenzhen Municipality
- National Key Research and Development Program of China
- Natural Science Foundation of Guangdong Province for Distinguished Young Scholars