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Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS 2 Transistors for Time-Series Processing.

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Title: Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS 2 Transistors for Time-Series Processing.
Authors: Wan, Xiang1 (AUTHOR), Yuan, Qiujie1,2 (AUTHOR), Sun, Lianze1,2 (AUTHOR), Chen, Kunfang1,2 (AUTHOR), Khim, Dongyoon1 (AUTHOR) dongyoonkhim@njupt.edu.cn, Luo, Zhongzhong2 (AUTHOR) luozz1012@njupt.edu.cn
Source: Polymers (20734360). May2025, Vol. 17 Issue 9, p1178. 11p.
Subjects: Reversible phase transitions, Standard deviations, Recognition (Psychology), Polyelectrolytes, Lithium ions
Abstract: This study presented a novel reservoir computing (RC) system based on polymer electrolyte-gated MoS2 transistors. The proposed transistors operate through lithium ion (Li+) intercalation, which induces reversible phase transitions between semiconducting 2H and metallic 1T' phases in MoS2 films. This mechanism enables dynamic conductance modulation with inherent nonlinearity and fading memory effects, rendering these transistors particularly suitable as reservoir nodes. Our RC implementation leverages time-multiplexed virtual nodes to reduce physical component requirements while maintaining rich temporal dynamics. Testing on a spoken digit recognition task using the NIST TI-46 dataset demonstrated 95.1% accuracy, while chaotic time-series prediction of the Lorenz system achieved a normalized root mean square error as low as 0.04. This work established polymer electrolyte-gated MoS2 transistors as promising building blocks for efficient RC systems capable of processing complex temporal patterns, offering enhanced scalability, and practical applicability in neuromorphic computation. [ABSTRACT FROM AUTHOR]
Copyright of Polymers (20734360) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS 2 Transistors for Time-Series Processing.
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  Data: <searchLink fieldCode="AR" term="%22Wan%2C+Xiang%22">Wan, Xiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuan%2C+Qiujie%22">Yuan, Qiujie</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Lianze%22">Sun, Lianze</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Kunfang%22">Chen, Kunfang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khim%2C+Dongyoon%22">Khim, Dongyoon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dongyoonkhim@njupt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Zhongzhong%22">Luo, Zhongzhong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> luozz1012@njupt.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Polymers+%2820734360%29%22">Polymers (20734360)</searchLink>. May2025, Vol. 17 Issue 9, p1178. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Reversible+phase+transitions%22">Reversible phase transitions</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Recognition+%28Psychology%29%22">Recognition (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Polyelectrolytes%22">Polyelectrolytes</searchLink><br /><searchLink fieldCode="DE" term="%22Lithium+ions%22">Lithium ions</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This study presented a novel reservoir computing (RC) system based on polymer electrolyte-gated MoS2 transistors. The proposed transistors operate through lithium ion (Li+) intercalation, which induces reversible phase transitions between semiconducting 2H and metallic 1T' phases in MoS2 films. This mechanism enables dynamic conductance modulation with inherent nonlinearity and fading memory effects, rendering these transistors particularly suitable as reservoir nodes. Our RC implementation leverages time-multiplexed virtual nodes to reduce physical component requirements while maintaining rich temporal dynamics. Testing on a spoken digit recognition task using the NIST TI-46 dataset demonstrated 95.1% accuracy, while chaotic time-series prediction of the Lorenz system achieved a normalized root mean square error as low as 0.04. This work established polymer electrolyte-gated MoS2 transistors as promising building blocks for efficient RC systems capable of processing complex temporal patterns, offering enhanced scalability, and practical applicability in neuromorphic computation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Polymers (20734360) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/polym17091178
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      – Code: eng
        Text: English
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        PageCount: 11
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      – SubjectFull: Reversible phase transitions
        Type: general
      – SubjectFull: Standard deviations
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      – SubjectFull: Recognition (Psychology)
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      – SubjectFull: Polyelectrolytes
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      – SubjectFull: Lithium ions
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      – TitleFull: Reservoir Computing Enabled by Polymer Electrolyte-Gated MoS 2 Transistors for Time-Series Processing.
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            NameFull: Wan, Xiang
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            NameFull: Yuan, Qiujie
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            NameFull: Sun, Lianze
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            NameFull: Chen, Kunfang
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            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
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