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The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
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Intrinsic semiconductors are highly pure materials with no impurities. At absolute zero, these semiconductors behave as perfect insulators because all the valence electrons are bound, and the conduction band is empty, disallowing electrical conduction. The Fermi level is a concept used to describe the probability of occupancy of energy levels by electrons at thermal equilibrium. In intrinsic semiconductors, the Fermi level is positioned at the midpoint of the energy gap at absolute zero. When...
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Metal chalcogenides for neuromorphic computing: emerging materials and mechanisms.

Sage R Bauers1, M Brooks Tellekamp1, Dennice M Roberts1

  • 1Materials Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, United States of America.

Nanotechnology
|April 21, 2021
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Summary

Metal chalcogenides show promise for efficient neuromorphic computing, mimicking the brain's low power usage. Research focuses on materials science and physical mechanisms for next-generation computing systems.

Keywords:
2D materialsTMDCscharge density wavesneuromorphic materialsphase change materials

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Moore's Law scaling limitations drive research into alternative computing paradigms.
  • Neuromorphic computing systems offer significant power efficiency advantages over conventional binary computers, inspired by the human brain's efficiency.
  • Metal chalcogenides are being investigated for their potential in neuromorphic applications due to their unique physical properties.

Purpose of the Study:

  • To review the materials science and physical mechanisms of metal chalcogenides for neuromorphic computing.
  • To compare biological neuronal mechanisms with experimental observations in chalcogenides.
  • To identify research gaps for advancing metal chalcogenides in neuromorphic systems.

Main Methods:

  • Review of biological signal generation and transduction in mammalian brains.
  • Analysis of experimental measurements in metal chalcogenides.
  • Focus on short-range physical mechanisms like structural phase changes and correlated electron systems.

Main Results:

  • Metal chalcogenides exhibit mechanisms (e.g., phase changes) suitable for low-energy stimuli-driven neuromorphic applications.
  • Comparison highlights similarities between biological neuronal functions and chalcogenide material behaviors.
  • Identified specific material properties and mechanisms relevant to energy-efficient computation.

Conclusions:

  • Metal chalcogenides are promising candidates for developing energy-efficient neuromorphic computing systems.
  • Further fundamental materials research is needed to bridge the gap between current capabilities and advanced neuromorphic integration.
  • Understanding short-range physical mechanisms in chalcogenides is key to unlocking their full potential for neuromorphic applications.