All-Fiber Synapse Utilizing Phase Change Materials for Information Recognition and Processing

Libo Yuan, Wei Jin, Xiang Li, Andriy Lotnyk, Jianzhong Zhang, Yaru Li, Zhihai Liu, Yu Zhang, Yaxun Zhang, Siying Cheng, Yifan Qin, Xinghua Yang
2023-11-02

SCID:  54.1/zx9w6whx
Optical fiber has been widely used in telecommunication networks due to its inherent advantages of considerable bandwidth and ultrahigh-speed light transmission. However, it only serves as the transmission medium and cannot identify and process data directly in the fiber. One solution is to simulate neuromorphic functions directly in fibers. Here, Ge 2 Sb 2 Te 5 -assisted all-fiber synapses are proposed for information recognition and processing. The synaptic spike-timing-dependent plasticity, an important synaptic learning rule in Hebbian theory, can be mimicked by controlling the taper extension of the tapered fiber and integrating Ge 2 Sb 2 Te 5 on its waist. The designed all-fiber synapse interacts efficiently with Ge 2 Sb 2 Te 5 to alter light transmission, expressed as the synaptic weight. This synapse exhibits 10-level weight, 22% transmission contrast ratio, and 180 ns fast switching time for a single pulse. Additionally, the character recognition and arithmetic operation functions are demonstrated based on the proposed all-fiber synapses, inspiring a new paradigm for in-fiber processing.
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2023-11-02
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Libo Yuan
Wei Jin
Xiang Li
Andriy Lotnyk
Jianzhong Zhang
Yaru Li
Zhihai Liu
Yu Zhang
Yaxun Zhang
Siying Cheng
Yifan Qin
Xinghua Yang
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