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Bio-Inspired In-Sensor Compression and Computing Based on Phototransistors.

Rui Wang1, Saisai Wang1, Kun Liang2

  • 1Key Laboratory of Wide Band Gap Semiconductor Technology, School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710071, China.

Small (Weinheim an Der Bergstrasse, Germany)
|May 9, 2022
PubMed
Summary

Indium-gallium-zinc-oxide phototransistors enable in-sensor compression and computing. This neuromorphic system achieves high recognition accuracy even with 50% signal compression, paving the way for efficient data processing.

Keywords:
in-sensor compressionin-sensor computingneuromorphic electronicsphototransistors

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Area of Science:

  • Materials Science
  • Neuromorphic Engineering
  • Optoelectronics

Background:

  • Biological nervous systems process information efficiently with partial signals.
  • Hardware implementing similar capabilities is crucial for sensor data dimension reduction and processing efficiency.
  • Indium-gallium-zinc-oxide (IGZO) thin film transistors offer potential for advanced information processing.

Purpose of the Study:

  • To demonstrate IGZO thin film phototransistors for in-sensor signal compression and computing.
  • To integrate phototransistor arrays for in-sensor compression and reservoir computing networks for in-sensor computing.
  • To develop a neuromorphic system for high-efficiency in-sensor compression and computing.

Main Methods:

  • Utilized IGZO thin film phototransistors exhibiting optoelectronic switching and light-tunable synaptic characteristics.
  • Implemented phototransistor arrays for in-sensor signal compression.
  • Developed a reservoir computing network using phototransistors for in-sensor computing.
  • Integrated compression and computing systems into a unified neuromorphic system.

Main Results:

  • Phototransistor arrays demonstrated effective in-sensor signal compression.
  • Reservoir computing networks implemented via phototransistors enabled in-sensor computing.
  • The integrated neuromorphic system achieved high-efficiency compression and computing.
  • Signal compression by 50% resulted in a reconstructed signal recognition accuracy of approximately 96%.

Conclusions:

  • IGZO phototransistors are suitable for optoelectronic switching and light-tunable synaptic functions.
  • The developed neuromorphic system enables efficient in-sensor compression and computing.
  • This technology holds promise for advancing human-computer interactions and the Internet of Things.