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超低能耗埃米流体忆阻器

Ultralow Energy Consumption Angstrom-Fluidic Memristor.

作者信息

Shi Deli, Wang Wenhui, Liang Yizheng, Duan Libing, Du Guanghua, Xie Yanbo

机构信息

School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, 710129, China.

Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China.

出版信息

Nano Lett. 2023 Dec 27;23(24):11662-11668. doi: 10.1021/acs.nanolett.3c03518. Epub 2023 Dec 8.

Abstract

The emergence of nanofluidic memristors has made a giant leap to mimic the neuromorphic functions of biological neurons. Here, we report neuromorphic signaling using Angstrom-scale funnel-shaped channels with poly-l-lysine (PLL) assembled at nano-openings. We found frequency-dependent current-voltage characteristics under sweeping voltage, which represents a diode in low frequencies, but it showed pinched current hysteresis as frequency increases. The current hysteresis is strongly dependent on pH values but weakly dependent on salt concentration. We attributed the current hysteresis to the entropy barrier of PLL molecules entering and exiting the Angstrom channels, resulting in reversible voltage-gated open-close state transitions. We successfully emulated the synaptic adaptation of Hebbian learning using voltage spikes and obtained a minimum energy consumption of 2-23 fJ in each spike per channel. Our findings pave a new way to mimic neuronal functions by Angstrom channels in low energy consumption.

摘要

纳米流体忆阻器的出现为模拟生物神经元的神经形态功能带来了巨大飞跃。在此,我们报告了利用埃尺度的漏斗形通道以及在纳米开口处组装的聚-L-赖氨酸(PLL)实现的神经形态信号传输。我们发现,在扫描电压下呈现出频率依赖的电流-电压特性,低频时表现为二极管特性,但随着频率增加,出现了电流滞回现象。电流滞回强烈依赖于pH值,而对盐浓度的依赖性较弱。我们将电流滞回归因于PLL分子进出埃通道的熵垒,从而导致可逆的电压门控开闭状态转变。我们成功地利用电压尖峰模拟了赫布学习的突触适应性,并且每个通道每次尖峰的最低能耗为2 - 23飞焦。我们的研究结果为通过埃通道以低能耗模拟神经元功能开辟了一条新途径。

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