Перейти к основному содержанию
AkademIndex

Продукты

Для разработчиков

AkademBaseОткрытый API экосистемы
Статья

Ultrafast Synaptic Events in a Chalcogenide Memristor

Yi LiWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, ChinaYingpeng ZhongSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, ChinaLei XuSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, ChinaJinjian ZhangSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, ChinaXiaohua XuSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, ChinaHuajun SunSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, ChinaXiangshui MiaoSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, China
2013en
ABI

Аннотация

Compact and power-efficient plastic electronic synapses are of fundamental importance to overcoming the bottlenecks of developing a neuromorphic chip. Memristor is a strong contender among the various electronic synapses in existence today. However, the speeds of synaptic events are relatively slow in most attempts at emulating synapses due to the material-related mechanism. Here we revealed the intrinsic memristance of stoichiometric crystalline Ge2Sb2Te5 that originates from the charge trapping and releasing by the defects. The device resistance states, representing synaptic weights, were precisely modulated by 30 ns potentiating/depressing electrical pulses. We demonstrated four spike-timing-dependent plasticity (STDP) forms by applying programmed pre- and postsynaptic spiking pulse pairs in different time windows ranging from 50 ms down to 500 ns, the latter of which is 10(5) times faster than the speed of STDP in human brain. This study provides new opportunities for building ultrafast neuromorphic computing systems and surpassing Von Neumann architecture.

Перевод пока недоступен

Идентификаторы

Цитирования и источники

Цитирований: 2Использованных источников: 0