Optically/electrically controlled Ag <sup>+</sup> metallization in solution-processed oxide memtransistors for neuromorphic computing

In this work, a solution-processable oxide-based memtransistor is designed for neuromorphic computing, incorporating LiInSnO<sub>4</sub> as the gate dielectric, SnO<sub>2</sub> as the semiconducting channel, and Ag<sup>+</sup>-exchanged LiV<sub>3</sub>O<sub>8</sub> as the resistive switching medium. The device demonstrates dual tunability in its channel conductance through both gate voltage and light modulation, enabling precise control over its switching characteristics. Operating at low voltages, the memtransistor achieves an LRS/HRS ratio of up to 10<sup>3</sup>, with stable performance across 10<sup>3</sup> switching cycles, over 10<sup>6</sup> pulse cycles, and retention up to 10<sup>5</sup> seconds. The device effectively replicates essential synaptic functions such as paired-pulse facilitation and short- and long-term plasticity, with ultra-low energy consumption: 193 pJ (0.1 fJ μm<sup>-2</sup>) optically and 540 pJ (0.3 fJ μm<sup>-2</sup>) electrically. It also shows low non-linearity in potentiation/depression events, 0.49/3.47 (optical) and 0.03/5.67 (electrical), facilitating accurate synaptic weight modulation. Light-driven logic operations and cognitive functions, learning, forgetting, and relearning are successfully demonstrated, along with Pavlovian classical conditioning. Neural network simulations confirm 98% and 95% recognition accuracy for optical and electrical synapses, while autoencoder-based denoising and data reconstruction further validate the applicability of the device in brain-inspired computing.

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Publication Details

Journal
Nanoscale
Published
2025-01-01
DOI
https://doi.org/10.1039/d5nr03811a
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19

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article

Optically/electrically controlled Ag + metallization in solution-processed oxide memtransistors for neuromorphic computing

Subarna Pramanik, Ranajit Ghosh, Bhola Nath Pal, Utkarsh Pandey et al.
3 citations
Nanoscale
Advanced Memory and Neural Computing
2.19
article

Optically/electrically controlled Ag + metallization in solution-processed oxide memtransistors for neuromorphic computing

Subarna Pramanik, Ranajit Ghosh, Bhola Nath Pal, Utkarsh Pandey, Akhilesh Kumar Yadav, Rajarshi Chakraborty, Himanshu Singodia
article en
3 citations

Abstract

In this work, a solution-processable oxide-based memtransistor is designed for neuromorphic computing, incorporating LiInSnO<sub>4</sub> as the gate dielectric, SnO<sub>2</sub> as the semiconducting channel, and Ag<sup>+</sup>-exchanged LiV<sub>3</sub>O<sub>8</sub> as the resistive switching medium. The device demonstrates dual tunability in its channel conductance through both gate voltage and light modulation, enabling precise control over its switching characteristics. Operating at low voltages, the memtransistor achieves an LRS/HRS ratio of up to 10<sup>3</sup>, with stable performance across 10<sup>3</sup> switching cycles, over 10<sup>6</sup> pulse cycles, and retention up to 10<sup>5</sup> seconds. The device effectively replicates essential synaptic functions such as paired-pulse facilitation and short- and long-term plasticity, with ultra-low energy consumption: 193 pJ (0.1 fJ μm<sup>-2</sup>) optically and 540 pJ (0.3 fJ μm<sup>-2</sup>) electrically. It also shows low non-linearity in potentiation/depression events, 0.49/3.47 (optical) and 0.03/5.67 (electrical), facilitating accurate synaptic weight modulation. Light-driven logic operations and cognitive functions, learning, forgetting, and relearning are successfully demonstrated, along with Pavlovian classical conditioning. Neural network simulations confirm 98% and 95% recognition accuracy for optical and electrical synapses, while autoencoder-based denoising and data reconstruction further validate the applicability of the device in brain-inspired computing.

NanoscaleVol. 17(47)
Indian Institute of Technology BHU (IN), Central Mechanical Engineering Research Institute (IN), Banaras Hindu University (IN)
Department of Science and Technology, Ministry of Science and Technology, India, Ministry of Education, India, Indian Institute of Technology (BHU) Varanasi, Science and Engineering Research Board
Openalex Percentile: Top 11%
Advanced Memory and Neural Computing
2.19
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