Quantum Dot Optoelectronic Synaptic Devices With Long Memory Time Enabled by Trap Density Regulation

Optoelectronic synaptic devices based on 0-D materials, such as quantum dots (QDs), have emerged for neuromorphic computing due to their unique photoelectric properties and low-cost solution-based manufacturing process. In the field of QD optoelectronic synaptic devices, traps are considered to play...

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Bibliographic Details
Published in:IEEE transactions on electron devices 2023-10, Vol.70 (10), p.1-8
Main Authors: Li, Zhicheng, Zhang, Ruiqi, Song, Zhulu, Sun, Jiayun, Wang, Zhaojin, Wu, Dan, Wang, Kai
Format: Article
Language:English
Subjects:
Cadmium compounds
Cadmium selenides
Companies
Electron traps
Energy consumption
heterojunction
Hybrid structures
II-VI semiconductor materials
Luminous intensity
Memory devices
Memory management
Optoelectronic devices
optoelectronic synaptic device
Photoelectric effect
Photoelectricity
Plastic properties
Quantum dots
Quantum dots (QDs)
Semiconductor device measurement
trap density
Zinc sulfide
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Summary:Optoelectronic synaptic devices based on 0-D materials, such as quantum dots (QDs), have emerged for neuromorphic computing due to their unique photoelectric properties and low-cost solution-based manufacturing process. In the field of QD optoelectronic synaptic devices, traps are considered to play an important role in memory. However, there are few studies on regulating traps to specify the memory ability of the device. Here, the relationship between traps and device memory ability is clarified through the research of optoelectronic synaptic devices based on the hybrid structure of CdSe/ZnS QDs and poly(3-hexylthiophene) (P3HT). By regulating the trap density on the surface of CdSe/ZnS QDs, the memory ability of devices is enhanced to \text{2} times, and the memory time of the device reaches up to 350 s, with low energy consumption of 29.2 pJ for conducting a synaptic activity. In addition, the device can emulate essential synaptic functions, such as excitatory postsynaptic current (EPSC), short-term plasticity (STP), and long-term plasticity (LTP), which can be manipulated by light intensity, light pulse interval, and the number of light pulses. Furthermore, in the simulation of image recognition where the device conductance values are utilized as synaptic weights, the recognition accuracy achieves 90.86%, and maintains above 80% after 160 s of forgetting process.
ISSN:0018-9383
1557-9646
DOI:10.1109/TED.2023.3303286