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.
Authors
- Subarna Pramanik (ORCID: https://orcid.org/0000-0002-1246-7535)
- Ranajit Ghosh (ORCID: https://orcid.org/0000-0003-2410-5459)
- Bhola Nath Pal (ORCID: https://orcid.org/0000-0001-7512-3441)
- Utkarsh Pandey (ORCID: https://orcid.org/0000-0001-6789-7033)
- Akhilesh Kumar Yadav (ORCID: https://orcid.org/0009-0001-3973-0540)
- Rajarshi Chakraborty (ORCID: https://orcid.org/0000-0003-4927-3168)
- Himanshu Singodia
Institutions
- Indian Institute of Technology BHU (IN)
- Central Mechanical Engineering Research Institute (IN)
- Banaras Hindu University (IN)
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
Funders
- 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