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用于节能神经形态电子学的仿生神经调质模拟的异突触 MoS 忆阻器

Heterosynaptic MoS Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics.

机构信息

KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

Department of Physics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

出版信息

Adv Mater. 2023 Jun;35(24):e2211525. doi: 10.1002/adma.202211525. Epub 2023 Apr 28.

Abstract

Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.

摘要

异突触神经调制是实现高能效和高层次生物神经处理的关键使能技术。然而,传统的忆阻器和突触晶体管无法模拟这种多样的突触调制。因此,本文报道了一种使用有意缺陷产生的二硫化钼通道的三端异突触 memtransistor。特别地,缺陷介导的超薄通道中的空间电荷限制传导导致源极和漏极之间的忆阻开关特性,根据栅极调谐的陷阶态填充进一步通过栅极端子进行调制。该器件充当人工突触,由源极和栅极端子的亚飞焦耳脉冲控制,比其生物对应物消耗的能量更少。特别地,静电栅极调制(对应于生物神经调制),独立于编程(源)脉冲,还调节了突触权重的动态范围和调谐速率。值得注意的是,这种异突触调制不仅提高了学习准确性和效率,而且降低了模式识别中的能量消耗。因此,该研究为实现高度网络化和高能效的神经形态电子学提供了一条新途径。

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