神经形态工程学
材料科学
线性
记忆电阻器
非线性系统
兴奋剂
突触
长时程增强
光电子学
热传导
人工神经网络
电子工程
人工智能
计算机科学
神经科学
复合材料
物理
受体
量子力学
工程类
生物化学
化学
生物
作者
Sridhar Chandrasekaran,Firman Mangasa Simanjuntak,R. Saminathan,Debashis Panda,Tseung‐Yuen Tseng
出处
期刊:Nanotechnology
[IOP Publishing]
日期:2019-08-20
卷期号:30 (44): 445205-445205
被引量:93
标识
DOI:10.1088/1361-6528/ab3480
摘要
Artificial synapse having good linearity is crucial to achieve an efficient learning process in neuromorphic computing. It is found that the synaptic linearity can be enhanced by engineering the doping region across the switching layer. The nonlinearity of potentiation and depression of the pure device is 36% and 91%, respectively; meanwhile, the nonlinearity after doping can be suppressed to be 22% (potentiation) and 60% (depression). Henceforth, the learning accuracy of the doped device is 91% with only 13 iterations; meanwhile, the pure device is 78%. A detailed conduction mechanism to understand this phenomenon is proposed.
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