Multifunctional Optoelectronic Memristor Based on MEH-PPV/C60 Heterojunction for Artificial Synapses and Bionic Visual System With Fear Sensing
Photoelectric memristors, with the advantages of low-power consumption, high scalability and high parallelism, have provided a way for broadening the application of memristors. Photoelectric memristors based on heterojunction structures have gained high-performance in simulating biological synaptic plasticity and visual synaptic bionics. However, the effective separation and migration process of photo-generated carriers and the carrier dynamics mechanism is still the key factor and urgently need development. In this study, we proposed an optoelectronic memristor based on the MEH-PPV/C60 heterostructure to simulate neural synaptic functions. Compared with the monolayer structure, the heterojunction devices exhibited an improvement in both optical and electrical performance. Various synaptic behaviors were successfully emulated under light and electric stimulation signals. The photo-luminescence (PL), time-resolved PL (TRPL) and transient absorption (TA) spectra measurements were utilized to analyze and reveal the carrier transport mechanisms of MEH-PPV/C60 heterojunction. Due to the effective electron-hole separation by the built-in electric field at the heterojunction interface, the memristor based on MEH-PPV/C60 heterojunction exhibited superior electrical and optical performance. A 3 × 4 memristor array was constructed to simulate the human visual system to perceive and remember images and an artificial visual system with fear sensing capability was presented, which achieved four key features of human visual fear receptors (threshold, no-adaptation, sensitization and relaxation). This work provides strategies for the development of future memristor structures and the construction of human-like visual systems.
Authors
- Changhai Zhu (ORCID: https://orcid.org/0000-0002-2683-4658)
- Jia Zhou (ORCID: https://orcid.org/0000-0001-5418-5141)
- Mingdong Yi (ORCID: https://orcid.org/0000-0002-9700-3368)
- Jie Yang (ORCID: https://orcid.org/0000-0001-9970-7505)
- Xiaowei Zhang (ORCID: https://orcid.org/0009-0005-4792-6868)
- Yexiong Huang (ORCID: https://orcid.org/0000-0001-9837-8295)
- Mingyu Pi (ORCID: https://orcid.org/0009-0009-5538-3428)
- Dingke Zhang (ORCID: https://orcid.org/0000-0003-0095-4368)
- Shao-Peng Su
- Jiangshan Chen (ORCID: https://orcid.org/0000-0002-0393-0057)
- Wen Li (ORCID: https://orcid.org/0000-0002-6695-4988)
Institutions
- Chongqing Normal University (CN)
- Nanjing University of Posts and Telecommunications (CN)
- State Key Laboratory of Luminescent Materials and Devices
- South China University of Technology (CN)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2025-06-16
- DOI
- https://doi.org/10.1109/jsen.2025.3578211
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Chongqing