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Object Motion Detection Enabled by Reconfigurable Neuromorphic Vision Sensor under Ferroelectric Modulation.

Zhaoying Dang1,2,3, Feng Guo1,2, Zhaoqing Wang1,4

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Summary
This summary is machine-generated.

This study introduces a novel vision sensor inspired by biological cells for efficient object motion detection (OMD). The device achieves zero power consumption and high accuracy, enabling advanced neuromorphic computing applications.

Keywords:
2D materialsferroelectricityhigh-linear nonvolatile multistatesneuromorphic vision sensorobject motion detection

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Object motion detection (OMD) demands reduced redundancy, improved power efficiency, and precise programming for accuracy.
  • Existing systems struggle to meet these demands, necessitating innovative solutions.

Purpose of the Study:

  • To propose a novel object motion detection (OMD) vision sensor inspired by biological ganglion cells.
  • To achieve zero power consumption, high accuracy, and synaptic weight emulation for neuromorphic applications.

Main Methods:

  • Developed a vision sensor using a WSe2 homojunction modulated by a ferroelectric copolymer.
  • Implemented progressive and bidirectional photocurrent generation with discrete multistates.
  • Emulated synaptic weight updating with 82 states and high linearity (R^2 of 0.999).

Main Results:

  • The vision sensor operates at zero power consumption, generating multistate photocurrents.
  • Demonstrated nonvolatility, reversibility, symmetry, and ultrahigh linearity (0.01/-0.01 nonlinearity).
  • Achieved 96.8% recognition accuracy in a neural network for motion detection.

Conclusions:

  • The proposed OMD vision sensor offers a controllable and energy-efficient platform for motion detection.
  • The device emulates synaptic plasticity, paving the way for advanced neuromorphic computing.
  • Scalable potential demonstrated via a 3x3 neuromorphic vision sensor array.