A high-speed 2D optoelectronic in-memory computing device with 6-bit storage and pattern recognition capabilities

被引:29
|
作者
Meng, Jialin [1 ]
Wang, Tianyu [1 ]
He, Zhenyu [1 ]
Li, Qingxuan [1 ]
Zhu, Hao [1 ,2 ]
Ji, Li [1 ,2 ]
Chen, Lin [1 ,2 ]
Sun, Qingqing [1 ,2 ]
Zhang, David Wei [1 ,2 ]
机构
[1] Fudan Univ, Sch Microelect, State Key Lab ASIC & Syst, Shanghai 200433, Peoples R China
[2] Natl Integrated Circuit Innovat Ctr, 825 Zhangheng Rd, Shanghai 201203, Peoples R China
基金
中国国家自然科学基金;
关键词
memory; multibit; two-dimensional (2D); MoSSe; Co-modulation; FIELD-EFFECT TRANSISTOR; NONVOLATILE MEMORY; CIRCUIT;
D O I
10.1007/s12274-021-3729-9
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The explosively developed era of big-data compels the increasing demand of nonvolatile memory with high efficiency and excellent storage properties. Herein, we fabricated a high-speed photoelectric multilevel memory device for neuromorphic computing. The novel two-dimensional (2D) MoSSe with a unique Janus structure was employed as the channel, and the stack of Al2O3/black phosphorus quantum dots (BPQDs)/Al2O3 was adopted as the dielectric. The storage performance of the resulting memory could be verified by the endurance and retention tests, in which the device could remain stable states of programming and erasing even after 1, 000 cycles and 1, 000 s. The multibit storage could be realized through both different voltage amplitudes and pulse numbers, which could achieve 6 bits (64 distinguishable levels) under pulse width of 50 ns. Furthermore, our memory device also could realize the simulations of synapses in human brain with optical and electric modulations synergistically, such as excitatory post-synaptic current (EPSC), long-term potentiation/depression (LTP/LTD), and spike-timing-dependent plasticity (STDP). Neuromorphic computing was successfully achieved through a high recognition of handwritten digits up to 92.5% after 10(3) epochs. This research is a promising avenue for the future development of efficient memory and artificial neural network systems.
引用
收藏
页码:2472 / 2478
页数:7
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