Ultralow-Power Compact Artificial Synapse Based on a Ferroelectric Fin Field-Effect Transistor for Spatiotemporal Information Processing,Advanced Intelligent Systems

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Ultralow-Power Compact Artificial Synapse Based on a Ferroelectric Fin Field-Effect Transistor for Spatiotemporal Information Processing
Advanced Intelligent Systems ( IF 6.8 ) Pub Date : 2023-08-23 , DOI: 10.1002/aisy.202300275
Zhaohao Zhang _{1,

2} , Guohui Zhan _{1,

2} , Weizhuo Gan _{1,

2} , Yan Cheng ₃ , Xumeng Zhang ₄ , Yue Peng ₅ , Jianshi Tang ₆ , Fan Zhang _{1,

5} , Jiali Huo _{1,

2} , Gaobo Xu ₁ , Qingzhu Zhang ₁ , Zhenhua Wu _{1,

2} , Yan Liu ₅ , Hangbing Lv _{1,

2} , Qi Liu ₄ , Genquan Han ₅ , Huaxiang Yin _{1,

2} , Jun Luo _{1,

2} , Wenwu Wang _{1,

2}

Affiliation

Artificial synapses are key elements in building bioinspired, neuromorphic computing systems. Ferroelectric field-effect transistors (FeFETs) with excellent controllability and complementary metal oxide semiconductor (CMOS) compatibility are favorable to achieving synaptic functions with low power consumption and high scalability. However, because of the only nonvolatile ferroelectric (Fe) characteristics in the FeFET, it is difficult to develop bioplausible short-term synaptic elements for spatiotemporal information processing. By judiciously combining defects (DE) and Fe domains in gate stacks, a compact artificial synapse featuring spatiotemporal information processing on a single Fe–DE fin FET (FinFET) is proposed. The devices are designed to work in a separate DE mode to induce short-term plasticity by spontaneous charge detrapping, and a hybrid Fe–DE mode to trigger long-term plasticity through the coupling of defects and Fe domains. The capability of the compact synapse is demonstrated by differentiating 16 temporal inputs. Moreover, the highly controllable static electricity of advanced FinFETs leads to an ultralow power of 2 fJ spike⁻¹. An all Fe–DE FinFET reservoir computing (RC) system is then constructed that achieves a recognition accuracy of 97.53% in digit classification. This work enables constructing RC systems with fully advanced CMOS-compatible devices featuring highly energy-efficient and low-hardware systems.

中文翻译：

基于铁电翅片场效应晶体管的超低功耗紧凑型人工突触，用于时空信息处理

人工突触是构建仿生神经形态计算系统的关键要素。铁电场效应晶体管（FeFET）具有优异的可控性和互补金属氧化物半导体（CMOS）兼容性，有利于实现低功耗和高可扩展性的突触功能。然而，由于 FeFET 中仅有非易失性铁电 (Fe) 特性，因此很难开发用于时空信息处理的生物合理的短期突触元件。通过明智地结合栅堆叠中的缺陷 (DE) 和 Fe 域，提出了一种紧凑的人工突触，其特征是在单个 Fe-DE 鳍 FET (FinFET) 上进行时空信息处理。该器件设计为在单独的 DE 模式下工作，通过自发电荷去俘获来诱导短期可塑性，并在混合 Fe-DE 模式下工作，通过缺陷和 Fe 域的耦合来触发长期可塑性。紧凑突触的能力通过区分 16 个时间输入来证明。此外，先进 FinFET 的高度可控静电可实现 2 fJ 尖峰^-1的超低功率。然后构建了全 Fe-DE FinFET 存储计算（RC）系统，在数字分类方面实现了 97.53% 的识别准确率。这项工作使得能够使用完全先进的 CMOS 兼容设备构建 RC 系统，这些设备具有高能效和低硬件系统。

更新日期：2023-08-23

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