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Improving linearity by introducing Al in HfO2 as a memristor synapse device

机译:通过在HFO2中引入作为Memristor Synapse设备来提高线性度

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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.
机译:具有良好线性度的人工突触对于在神经形态计算中实现有效的学习过程至关重要。 发现可以通过将掺杂区域横跨开关层来增强突触线性。 纯装置的增强和抑郁的非线性分别为36%和91%; 同时,掺杂后的非线性可以抑制为22%(增强)和60%(抑郁症)。 从此,掺杂器件的学习精度为91%,只有13个迭代; 同时,纯设备为78%。 提出了理解这种现象的详细传导机制。

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