Ultralow Energy Consumption Angstrom-Fluidic Memristor
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–voltag...
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Veröffentlicht in: | Nano letters 2023-12, Vol.23 (24), p.11662-11668 |
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creator | Shi, Deli Wang, Wenhui Liang, Yizheng Duan, Libing Du, Guanghua Xie, Yanbo |
description | 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. |
doi_str_mv | 10.1021/acs.nanolett.3c03518 |
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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. 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title | Ultralow Energy Consumption Angstrom-Fluidic Memristor |
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