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Mimicking biological neurons with a nanoscale ferroelectric transistor†
Nanoscale ( IF 5.8 ) Pub Date : 2018-11-01 00:00:00 , DOI: 10.1039/c8nr07135g Halid Mulaosmanovic 1, 2, 3 , Elisabetta Chicca 3, 4, 5, 6, 7 , Martin Bertele 3, 4, 5, 6, 7 , Thomas Mikolajick 1, 2, 3, 8, 9 , Stefan Slesazeck 1, 2, 3
Nanoscale ( IF 5.8 ) Pub Date : 2018-11-01 00:00:00 , DOI: 10.1039/c8nr07135g Halid Mulaosmanovic 1, 2, 3 , Elisabetta Chicca 3, 4, 5, 6, 7 , Martin Bertele 3, 4, 5, 6, 7 , Thomas Mikolajick 1, 2, 3, 8, 9 , Stefan Slesazeck 1, 2, 3
Affiliation
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Neuron is the basic computing unit in brain-inspired neural networks. Although a multitude of excellent artificial neurons realized with conventional transistors have been proposed, they might not be energy and area efficient in large-scale networks. The recent discovery of ferroelectricity in hafnium oxide (HfO2) and the related switching phenomena at the nanoscale might provide a solution. This study employs the newly reported accumulative polarization reversal in nanoscale HfO2-based ferroelectric field-effect transistors (FeFETs) to implement two key neuronal dynamics: the integration of action potentials and the subsequent firing according to the biologically plausible all-or-nothing law. We show that by carefully shaping electrical excitations based on the particular nucleation-limited switching kinetics of the ferroelectric layer further neuronal behaviors can be emulated, such as firing activity tuning, arbitrary refractory period and the leaky effect. Finally, we discuss the advantages of an FeFET-based neuron, highlighting its transferability to advanced scaling technologies and the beneficial impact it may have in reducing the complexity of neuromorphic circuits.
中文翻译：
用纳米铁电晶体管模仿生物神经元†
神经元是灵感来自大脑的神经网络的基本计算单元。尽管已经提出了用常规晶体管实现的许多优秀的人工神经元，但它们在大规模网络中可能没有能源和面积效率。氧化ha（HfO 2）中铁电的最新发现以及相关的纳米级开关现象可能提供解决方案。这项研究采用了最新报道的纳米级HfO 2中的累积极化反转的铁电场效应晶体管（FeFET）来实现两个关键的神经元动力学：动作电位的整合和随后的根据生物学上可能出现的全有或全无定律的触发。我们表明，通过基于铁电层的特定成核限制开关动力学仔细地整形电激发，可以模拟进一步的神经元行为，例如发射活动调整，任意不应期和泄漏效应。最后，我们讨论了基于FeFET的神经元的优势，着重介绍了其向先进缩放技术的可移植性，以及它在降低神经形态电路的复杂性方面可能产生的有益影响。
更新日期：2018-11-01
中文翻译：
用纳米铁电晶体管模仿生物神经元†
神经元是灵感来自大脑的神经网络的基本计算单元。尽管已经提出了用常规晶体管实现的许多优秀的人工神经元，但它们在大规模网络中可能没有能源和面积效率。氧化ha（HfO 2）中铁电的最新发现以及相关的纳米级开关现象可能提供解决方案。这项研究采用了最新报道的纳米级HfO 2中的累积极化反转的铁电场效应晶体管（FeFET）来实现两个关键的神经元动力学：动作电位的整合和随后的根据生物学上可能出现的全有或全无定律的触发。我们表明，通过基于铁电层的特定成核限制开关动力学仔细地整形电激发，可以模拟进一步的神经元行为，例如发射活动调整，任意不应期和泄漏效应。最后，我们讨论了基于FeFET的神经元的优势，着重介绍了其向先进缩放技术的可移植性，以及它在降低神经形态电路的复杂性方面可能产生的有益影响。
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