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Ferroelectric materials for neuroinspired computing applications.

Dong Wang1,2, Shenglan Hao1, Brahim Dkhil3

  • 1Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China.

Fundamental Research
|October 21, 2024
PubMed
Summary
This summary is machine-generated.

Ferroelectric memory devices offer a solution to the limitations of traditional computing architectures for artificial intelligence (AI). These devices enable efficient in-memory and in-sensor computing, paving the way for advanced AI applications.

Keywords:
Artificial neural networkFerroelectric materialsFerroelectric synaptic devicesIn-memory computingIn-sensor computing

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Conventional von Neumann architecture faces limitations due to discrete data processing, hindering efficient artificial intelligence (AI) applications.
  • The 'memory wall' problem necessitates novel computing architectures and materials for sustainable AI processing.
  • In-memory and in-sensor computing, inspired by neurobiology, offer potential solutions.

Purpose of the Study:

  • To explore ferroelectric memory devices as a viable material basis for overcoming von Neumann architecture limitations.
  • To review the development, mechanisms, and applications of ferroelectric synaptic devices in artificial neural networks.
  • To present recent advancements in ferroelectric-based in-memory and in-sensor computing, focusing on CMOS compatibility.

Main Methods:

  • Review of ferroelectric material development and polarization reversal mechanisms.
  • Analysis of ferroelectric synaptic devices for artificial neural networks.
  • Examination of recent progress in ferroelectrics-based in-memory and in-sensor computing.
  • Focus on hafnium-based ferroelectric memory devices and their CMOS process compatibility.

Main Results:

  • Ferroelectric memory devices exhibit non-volatile polarization, low power consumption, and high endurance, making them suitable for neuromorphic computing.
  • Significant progress has been achieved in CMOS compatibility for ferroelectric memory devices.
  • Ferroelectric synaptic devices show promise for efficient implementation in artificial neural networks.

Conclusions:

  • Ferroelectric memory devices are promising candidates for next-generation computing architectures required for advanced AI.
  • Continued research into ferroelectric materials and their integration with CMOS technology is crucial for future developments in AI hardware.
  • Ferroelectric-based in-memory and in-sensor computing represent a significant advancement towards efficient and sustainable AI processing.