Controllable Charge Storability in InP/ZnSe Core/Shell Quantum Dots toward Bioinspired Optical Synaptic Application
Optoelectronic materials with proper charge storage play a pivotal role in the development of artificial neuromorphic devices aiming to mimic the visual, sensory, and memory functions of the human nervous system. This study presents the controllable charge storability in Indium Phosphide quantum dots through being capped with Zinc Selenide shells of different thicknesses. The organic transistors with the quantum dots integrated demonstrate shell-thickness-dependent optoelectronic memory characteristics, featuring optically programmable-electrically erasable channel states. Analysis reveals that the optoelectronic performance of the device is ascribed to the photoexcitation and the following charge storage process in the quantum dots. The device of the thickest quantum-dot-shell performs well as an optoelectronic synapse to emulate the entire human visual sensory and memory function. The frequency-dependent synaptic potentiation/depression, paired-pulse facilitation, short/long-term memory, and "learning-experience" behavior are exhibited in the optoelectronic synaptic device through optical stimuli manipulation. Moreover, the optical sensory performance of the device can be enhanced by a positive gate bias. It enables a successful emulation of Pavlov's dog classical conditioning experiments, realizing the associative learning characteristic with optical and electric signals. This work provides an effective solution for a stable and controllable charge storage medium for optoelectronic synapse applications.
Authors
- Botao Ji (ORCID: https://orcid.org/0000-0002-5775-7421)
- Bingbing Huo
- Zheng Xiang (ORCID: https://orcid.org/0000-0001-8161-5194)
- Zhiguo Chi
- Honglei Wu (ORCID: https://orcid.org/0000-0002-8852-3951)
- Guohao Wen
- Zhenhua Sun (ORCID: https://orcid.org/0000-0002-9227-6738)
- Dingting Zheng
- Guanlin Ke
Institutions
- Shenzhen University (CN)
- Westlake University (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1002/advs.202511714
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73