Chandrasekaran Sridhar, Simanjuntak Firman Mangasa, Saminathan R, Panda Debashis, Tseng Tseung-Yuen
Department of Electrical Engineering and Computer Science, National Chiao Tung University, Hsinchu 30010, Taiwan.
Nanotechnology. 2019 Nov 1;30(44):445205. doi: 10.1088/1361-6528/ab3480.
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.
具有良好线性度的人工突触对于在神经形态计算中实现高效学习过程至关重要。研究发现,通过设计跨越开关层的掺杂区域可以增强突触线性度。纯器件的增强和抑制非线性分别为36%和91%;同时,掺杂后的非线性可抑制至22%(增强)和60%(抑制)。此后,掺杂器件仅需13次迭代学习精度即可达到91%;而纯器件为78%。本文提出了一种详细的传导机制来解释这一现象。