Thin film ferroelectric photonic-electronic memory
To reduce system complexity and bridge the interface between electronic and photonic circuits, there is a high demand for a non-volatile memory that can be accessed both electrically and optically. However, practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible integration. Here, we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring resonator. We successfully demonstrate programming and erasing the memory using both electrical and optical methods, assisted by optical-to-electrical-to-optical conversion. The memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4 × 10<sup>4</sup> cycles. Furthermore, the multi-level storage capability is analyzed in detail, revealing stable performance with a raw bit-error-rate smaller than 5.9 × 10<sup>-2</sup>. This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems, targeting a wide range of applications such as photonic interconnect, high-speed data communication, and neuromorphic computing.
Authors
- Zijie Zheng (ORCID: https://orcid.org/0000-0001-8265-6019)
- Leming Jiao (ORCID: https://orcid.org/0000-0002-2562-7630)
- Yue Chen (ORCID: https://orcid.org/0000-0003-4323-0001)
- Jiezhi Chen (ORCID: https://orcid.org/0000-0003-2996-1406)
- Jiuren Zhou (ORCID: https://orcid.org/0000-0002-0915-5354)
- Qiwen Kong
- Kai Ni (ORCID: https://orcid.org/0000-0002-3628-3431)
- Gong Zhang (ORCID: https://orcid.org/0000-0002-7749-6177)
- Zuopu Zhou (ORCID: https://orcid.org/0000-0003-4812-8524)
- Jixuan Wu (ORCID: https://orcid.org/0000-0002-3207-9724)
- Haibo Wang (ORCID: https://orcid.org/0000-0001-6201-9274)
- Chen Sun (ORCID: https://orcid.org/0000-0001-5695-7803)
- Rui Shao (ORCID: https://orcid.org/0009-0003-5952-4757)
- Jishen Zhang (ORCID: https://orcid.org/0009-0008-6007-2539)
- Xiao Gong
Institutions
- Shandong University (CN)
- Rochester Institute of Technology (US)
- National University of Singapore (SG)
Publication Details
- Journal
- Light Science & Applications
- Published
- 2024-08-23
- DOI
- https://doi.org/10.1038/s41377-024-01555-6
- Citations
- 20
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 6.95
Funders
- National Research Foundation
- National Research Foundation Singapore
- Ministry of Education - Singapore
- Ministry of Education, India