神经形态工程学
材料科学
光子学
晶体管
光电子学
钙钛矿(结构)
量子点
神经促进
纳米技术
计算机科学
兴奋性突触后电位
神经科学
电气工程
电压
人工智能
工程类
人工神经网络
抑制性突触后电位
生物
化学工程
作者
Congyong Wang,Qisheng Sun,Gang Peng,Yujie Yan,Xipeng Yu,Enlong Li,Rengjian Yu,Changsong Gao,Xiaotao Zhang,Shuming Duan,Huipeng Chen,Jishan Wu,Wenping Hu
标识
DOI:10.1007/s40843-022-2200-0
摘要
Photonic synaptic transistors are promising neuromorphic computing systems that are expected to circumvent the intrinsic limitations of von Neumann-based computation. The design and construction of photonic synaptic transistors with a facile fabrication process and high-efficiency information processing ability are highly desired, while it remains a tremendous challenge. Herein, a new approach based on spin coating of a blend of CsPbBr3 perovskite quantum dot (QD) and PDVT-10 conjugated polymer is reported for the fabrication of photonic synaptic transistors. The combination of flat surface, outstanding optical absorption, and remarkable charge transporting performance contributes to high-efficiency photon-to-electron conversion for such perovskite-based synapses. High-performance photonic synaptic transistors are thus fabricated with essential synaptic functionalities, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and long-term memory. By utilizing the photonic potentiation and electrical depression features, perovskite-based photonic synaptic transistors are also explored for neuromorphic computing simulations, showing high pattern recognition accuracy of up to 89.98%, which is one of the best values reported so far for synaptic transistors used in pattern recognition. This work provides an effective and convenient pathway for fabricating perovskite-based neuromorphic systems with high pattern recognition accuracy.
科研通智能强力驱动
Strongly Powered by AbleSci AI