Photonic-Mediated Neuromorphic Computing Enabled by a Copper Oxide Microcrystal Optoelectronic Synapse
The concept of photonic neuromorphic computing offers fast, energy-efficient, and autonomous data processing yet faces challenges in the design of an active material, enabling the desired performance. Here, we demonstrate a copper oxide microcrystal optical synapse, demonstrating efficient, fast, and highly enhanced photonic neuromorphic computing. By optically pumping a single microcrystal with 2.3 eV photons, we observe a history-dependent response of photoexcited electrons (spike), controlled by the pumping repetition rate. This neuromorphic behavior exhibits a 1 ms spike response time, a 10<sup>2</sup> on/off ratio, exceptional endurance over 13,400 cycles, and allows achieving 95% accuracy in handwritten digit recognition in three training epochs. The reported optical synapse surpasses most existing designs, paving the way for efficient and long-lasting photonic neuromorphic data processing.
Authors
- Maria Timofeeva (ORCID: https://orcid.org/0000-0002-8156-5411)
- Vladimir Shirobokov
- Valentin A. Milichko (ORCID: https://orcid.org/0000-0002-8461-0804)
- Svyatoslav A. Povarov (ORCID: https://orcid.org/0000-0002-4504-6593)
- Semyon V. Bachinin
- Alena N. Kulakova (ORCID: https://orcid.org/0000-0003-0483-6502)
- Alexandra Gavrilova
Institutions
- ITMO University (RU)
- Institut Jean Lamour (FR)
- Université de Lorraine (FR)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1021/acsami.5c06829
- Citations
- 2
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 4.96
Funders
- Agence Nationale de la Recherche
- Ministry of Education and Science of the Russian Federation
- ITMO University