论文标题
通过工作功能工程改进FTJ，用于大规模平行神经形态计算
Improvement of FTJ on-current by work function engineering for massive parallel neuromorphic computing
论文作者
论文摘要
基于HFO2的铁电隧道连接（FTJ）具有在神经形态应用中采用的吸引力。超低功率多级开关功能以及低电流密度的组合表明，在电路中应用了大规模平行计算。在这项工作中，我们讨论了具有多个平行连接的FTJ设备的差分突触单元的一个示例电路。此外，从电路要求来看，我们推断出电流的绝对差异（ION -IOFF）比隧道电气固定比（TER）更为关键。基于此，我们通过BiLayer Hzo/Al2O3 FTJ中的电极工作功能工程来讨论FTJ设备优化的潜力。
HfO2-based ferroelectric tunnel junctions (FTJs) exhibit attractive properties for adoption in neuromorphic applications. The combination of ultra-low-power multi-level switching capability together with the low on-current density suggests the application in circuits for massive parallel computation. In this work, we discuss one example circuit of a differential synaptic cell featuring multiple parallel connected FTJ devices. Moreover, from the circuit requirements we deduce that the absolute difference in currents (Ion - Ioff) is a more critical figure of merit than the tunneling electroresistance ratio (TER). Based on this, we discuss the potential of FTJ device optimization by means of electrode work function engineering in bilayer HZO/Al2O3 FTJs.
