Ultralow‐Power Peptide‐Based Memristor Enabled by Emulation of Proton‐Mediated Synaptic Signaling

The substantial power consumption of traditional computing architectures, arising from the physical separation of memory and processing units, has motivated the exploration of neuromorphic systems that emulate the remarkable energy efficiency of the human brain. However, artificial neural networks implemented on neuromorphic devices still require billions of weight updates, and conventional devices typically consume power in the milliwatt range per switching event, posing a significant challenge even for neuromorphic systems. In this study, a synapse-like memristive device utilizing a tyrosine-rich peptide is presented as the resistive switching layer. By emulating proton-mediated signaling of biological synapses, the device leverages proton-electron dual-carrier transport enabled by the redox-active properties of tyrosine to realize ultralow-power resistive switching. Proton modulation is implemented through two methods: i) exposure to external humidity and ii) electrically driven injection using a PdH<sub>x</sub> proton reservoir layer. Comparative analysis reveals that the electrically driven approach achieves an ultralow switching power of 215 pW-≈2500 times lower than that of the intrinsic device-primarily owing to the sustained low off-current during proton injection. These results demonstrate a promising strategy for developing highly energy-efficient, synapse-like memristive devices through precise control of proton dynamics.

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Publication Details

Journal
Small Methods
Published
2025-10-24
DOI
https://doi.org/10.1002/smtd.202501472
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

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article

Ultralow‐Power Peptide‐Based Memristor Enabled by Emulation of Proton‐Mediated Synaptic Signaling

Min‐Kyu Song, Seok Daniel Namgung, Jang‐Yeon Kwon, Wooho Ham et al.
1 citations
Small Methods
Advanced Memory and Neural Computing
0.73
article

Ultralow‐Power Peptide‐Based Memristor Enabled by Emulation of Proton‐Mediated Synaptic Signaling

Min‐Kyu Song, Seok Daniel Namgung, Jang‐Yeon Kwon, Wooho Ham, Jeong-Min Park, Jeong Hyun Yoon
article en
1 citations

Abstract

The substantial power consumption of traditional computing architectures, arising from the physical separation of memory and processing units, has motivated the exploration of neuromorphic systems that emulate the remarkable energy efficiency of the human brain. However, artificial neural networks implemented on neuromorphic devices still require billions of weight updates, and conventional devices typically consume power in the milliwatt range per switching event, posing a significant challenge even for neuromorphic systems. In this study, a synapse-like memristive device utilizing a tyrosine-rich peptide is presented as the resistive switching layer. By emulating proton-mediated signaling of biological synapses, the device leverages proton-electron dual-carrier transport enabled by the redox-active properties of tyrosine to realize ultralow-power resistive switching. Proton modulation is implemented through two methods: i) exposure to external humidity and ii) electrically driven injection using a PdH<sub>x</sub> proton reservoir layer. Comparative analysis reveals that the electrically driven approach achieves an ultralow switching power of 215 pW-≈2500 times lower than that of the intrinsic device-primarily owing to the sustained low off-current during proton injection. These results demonstrate a promising strategy for developing highly energy-efficient, synapse-like memristive devices through precise control of proton dynamics.

Small MethodsVol. 9(12)
Yonsei University (KR), Korea University (KR), Korea University (JP), Chung-Ang University (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Education, India, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
0.73
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