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Related Experiment Video

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Emerging Materials for Neuromorphic Devices and Systems.

Min-Kyu Kim1, Youngjun Park1, Ik-Jyae Kim1

  • 1Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea.

Iscience
|December 15, 2020
PubMed
Summary
This summary is machine-generated.

Neuromorphic devices offer efficient computing by mimicking brain functions. This review explores emerging materials for energy-efficient hardware, addressing limitations in current machine learning and complementary metal-oxide-semiconductor approaches.

Keywords:
DevicesElectronic MaterialsMaterials DesignMemory Structure

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Neuroscience

Background:

  • Neuromorphic computing systems are explored for next-generation, efficient data processing.
  • Current systems using machine learning software and CMOS hardware face challenges in power consumption and learning speed.
  • Developing hardware that mimics brain functions is crucial for energy-efficient neuromorphic computing.

Purpose of the Study:

  • To review recent advances in neuromorphic devices and systems.
  • To discuss the functions of biological synapses and neurons.
  • To describe deep neural networks and spiking neural networks.

Main Methods:

  • Review of biological synapse and neuron functions.
  • Description of deep neural networks and spiking neural networks.
  • Analysis of the operation mechanisms and functions of emerging neuromorphic devices.

Main Results:

  • Emerging materials are being investigated for neuromorphic device development.
  • Novel devices demonstrate potential for mimicking brain functions.
  • Challenges and prospects for material-based neuromorphic devices are identified.

Conclusions:

  • Emerging materials are key to overcoming limitations in current neuromorphic hardware.
  • Further research into novel materials will drive the development of efficient brain-inspired computing systems.
  • Neuromorphic devices hold promise for future high-performance, low-power computation.