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This study introduces a novel memristor for in-sensor computing, enabling low-power, low-latency gas sensing. The device integrates neuromorphic functions for efficient hazardous gas monitoring and control.

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Traditional gas sensors face efficiency issues due to von Neumann architectures.
  • There is a need for low-power, low-latency olfactory systems with in-sensor computing capabilities.

Purpose of the Study:

  • To propose a reconfigurable neuromorphic heterostructure memristor (MXene@SnS2@PANI) for in-sensor computing.
  • To develop an olfactory system with integrated computational functions for gas sensing.

Main Methods:

  • Fabrication of a MXene@SnS2@PANI heterostructure memristor.
  • Demonstration of reconfigurable responses to electrical and gas stimuli.
  • Implementation of integrate-and-fire neuronal dynamics and neuromorphic synaptic behaviors.

Main Results:

  • The memristor exhibits both synaptic and neuronal computational functions in a single device.
  • Reconfigurable neuromorphic computing features with volatile and nonvolatile conductance updates were achieved.
  • Successful real-time hazardous gas monitoring and automated ventilation control were demonstrated.

Conclusions:

  • The proposed device offers a novel approach to neuromorphic olfactory systems, minimizing circuit complexity.
  • This technology paves the way for next-generation intelligent sensing systems.
  • The system enables efficient gas recognition and concentration identification through neuromorphic computing.