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

Rajarshi Chakraborty1, Himanshu Singodia1, Subarna Pramanik1

  • 1School of Materials Science and Technology, Indian Institute of Technology (Banaras Hindu University) Varanasi, Varanasi-221005, Uttar Pradesh, India. bnpal.mst@iitbhu.ac.in.

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Summary
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This study introduces a novel oxide-based memtransistor for neuromorphic computing, offering dual optical and electrical control for synaptic functions. The device demonstrates high stability and low energy consumption, paving the way for efficient brain-inspired computing systems.

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Area of Science:

  • Materials Science
  • Electronics
  • Computer Science

Background:

  • Neuromorphic computing aims to mimic the human brain's structure and function.
  • Memristive devices are key components for building artificial synapses.
  • Developing efficient and stable memristors is crucial for advancing neuromorphic systems.

Purpose of the Study:

  • To design and demonstrate a solution-processable oxide-based memtransistor for neuromorphic computing.
  • To achieve dual tunability of channel conductance via gate voltage and light modulation.
  • To evaluate the device's performance in replicating synaptic functions and enabling cognitive tasks.

Main Methods:

  • Fabrication of a memtransistor using LiInSnO4, SnO2, and Ag+-exchanged LiV3O8.
  • Characterization of device performance under electrical and optical stimuli.
  • Testing of synaptic functions like paired-pulse facilitation and plasticity.
  • Demonstration of light-driven logic and cognitive functions.
  • Neural network simulations for recognition accuracy assessment.

Main Results:

  • The memtransistor exhibits dual tunability and operates at low voltages with an LRS/HRS ratio up to 10^3.
  • Stable performance demonstrated over 10^3 switching cycles, 10^6 pulse cycles, and 10^5 seconds retention.
  • Replication of synaptic functions with ultra-low energy consumption (193 pJ optical, 540 pJ electrical).
  • Successful demonstration of light-driven logic, cognitive functions, and Pavlovian conditioning.
  • Achieved 98% (optical) and 95% (electrical) recognition accuracy in neural network simulations.

Conclusions:

  • The developed oxide-based memtransistor is a promising candidate for efficient neuromorphic computing.
  • Dual electrical and optical modulation offers precise control over synaptic weight modulation.
  • The device's ability to perform complex cognitive functions highlights its potential for brain-inspired AI.