Synaptic Transistor Capable of Accelerated Learning Induced by Temperature-Facilitated Modulation of Synaptic Plasticity

• Published in: ACS applied materials & interfaces 2019-12, Vol.11 (49), p.46008-46016
• Main Authors: Li, Enlong, Lin, Weikun, Yan, Yujie, Yang, Huihuang, Wang, Xiumei, Chen, Qizhen, Lv, DongXu, Chen, Gengxu, Chen, Huipeng, Guo, Tailiang
• Format: Article
• Language: English
• Subjects: Artificial Intelligence - trends; Brain - physiology; Brain Chemistry; Humans; Learning; Neuronal Plasticity - physiology; Synapses - chemistry; Synapses - physiology; Temperature; Transistors, Electronic
• ISSN: 1944-8244; 1944-8252
• DOI: 10.1021/acsami.9b17227

Neuromorphic computation, which emulates the signal process of the human brain, is considered to be a feasible way for future computation. Realization of dynamic modulation of synaptic plasticity and accelerated learning, which could improve the processing capacity and learning ability of artificial synaptic devices, is considered to further improve energy efficiency of neuromorphic computation. Nevertheless, realization of dynamic regulation of synaptic weight without an external regular terminal and the method that could endow artificial synaptic devices with the ability to modulate learning speed have rarely been reported. Furthermore, finding suitable materials to fully mimic the response of photoelectric stimulation is still challenging for photoelectric synapses. Here, a floating gate synaptic transistor based on an L-type ligand-modified all-inorganic CsPbBr3 perovskite quantum dots is demonstrated. This work provides first clear experimental evidence that the synaptic plasticity can be dynamically regulated by changing the waveforms of action potential and the environment temperature and both of these parameters originate from and are crucial in higher organisms. Moreover, benefiting from the excellent photoelectric properties and stability of quantum dots, a temperature-facilitated learning process is illustrated by the classical conditioning experiment with and without illumination, and the mechanism of synaptic plasticity is also demonstrated. This work offers a feasible way to realize dynamic modulation of synaptic weight and accelerating the learning process of artificial synapses, which showed great potential in the reduction of energy consumption and improvement of efficiency of future neuromorphic computing.