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首页> 外文期刊>Scientific reports. >Ferroelectric Tunneling Junctions Based on Aluminum Oxide/ Zirconium-Doped Hafnium Oxide for Neuromorphic Computing
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Ferroelectric Tunneling Junctions Based on Aluminum Oxide/ Zirconium-Doped Hafnium Oxide for Neuromorphic Computing

机译:基于氧化铝/锆掺杂氧化铪的铁电隧穿接插用于神经形态计算

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Ferroelectric tunneling junctions (FTJs) with tunable tunneling electroresistance (TER) are promising for many emerging applications, including non-volatile memories and neurosynaptic computing. One of the key challenges in FTJs is the balance between the polarization value and the tunneling current. In order to achieve a sizable on-current, the thickness of the ferroelectric layer needs to be scaled down below 5?nm. However, the polarization in these ultra-thin ferroelectric layers is very small, which leads to a low tunneling electroresistance (TER) ratio. In this paper, we propose and demonstrate a new type of FTJ based on metal/Alsub2/subOsub3/sub/Zr-doped HfOsub2/sub/Si structure. The interfacial Alsub2/subOsub3/sub layer and silicon substrate enable sizable TERs even when the thickness of Zr-doped HfOsub2/sub (HZO) is above 10?nm. We found that F-N tunneling dominates at read voltages and that the polarization switching in HZO can alter the effective tunneling barrier height and tune the tunneling resistance. The FTJ synapses based on Alsub2/subOsub3/sub/HZO stacks show symmetric potentiation/depression characteristics and widely tunable conductance. We also show that spike-timing-dependent plasticity (STDP) can be harnessed from HZO based FTJs. These novel FTJs will have high potential in non-volatile memories and neural network applications.
机译:具有可调谐隧道电钻(TER)的铁电隧穿结(FTJS)是许多新兴应用的承诺,包括非易失性存储器和神经突触计算。 FTJS中的一个关键挑战是极化值与隧道电流之间的平衡。为了实现相当大的电流,铁电层的厚度需要缩小5?nm以下。然而,这些超薄铁电层中的偏振非常小,这导致低隧道电气探测(TER)比率。在本文中,我们提出并证明了基于金属/ Al 2 O 3 / zr-掺杂的Hfo 2 / si的新型FTJ结构体。界面AL 2 O 3 层和硅衬底,即使当Zr掺杂的HFO 2 (Hzo)的厚度高于10时,也能够均匀的速度?nm。我们发现F-N隧道在读取电压下占主导地位,并且HZO中的极化切换可以改变有效的隧道屏障高度并调谐隧道电阻。基于Al 2 O 3 / hzo堆叠的FTJ突触显示了对称电压/凹陷特性和广泛的可调电导。我们还表明,可以从基于HZO的FTJS利用Spike时序依赖性可塑性(STDP)。这些新型FTJS在非易失性存储器和神经网络应用中具有很高的潜力。

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