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Conductive-bridging random-access memories (CBRAMs) offer advantages over oxide memristors for artificial neural networks (ANNs). This review details CBRAM operation, applications in ANNs, and future potential for large-scale systems.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Neuroscience

Background:

  • The von Neumann architecture faces limitations for artificial intelligence (AI) workloads.
  • Memristors show promise for neuromorphic computing due to their bio-inspired electrical properties.
  • Oxide-based memristors are common in hardware artificial neural networks (ANNs) due to CMOS compatibility.

Purpose of the Study:

  • To review conductive-bridging random-access memories (CBRAMs) as a superior alternative to oxide memristors for neuromorphic computing.
  • To detail the operational principles and applications of CBRAMs in artificial neural networks.
  • To highlight the potential of CBRAMs for next-generation AI hardware.

Main Methods:

  • Examination of CBRAM operational basics, including metal nanocluster formation and filament bridging.
  • Review of experimental demonstrations of CBRAM-based artificial synapses and neurons.
  • Discussion of CBRAM applications in deep neural networks and spiking neural networks.

Main Results:

  • CBRAMs possess advantages over oxide memristors, including high scalability, wide dynamic range, and low off-current.
  • CBRAMs have been experimentally demonstrated as effective artificial synapses and neurons.
  • CBRAMs show potential for implementing complex ANNs and other computing applications.

Conclusions:

  • CBRAMs are a promising technology for overcoming the limitations of current computing paradigms for AI.
  • Further research into CBRAMs is essential for developing advanced neuromorphic computing systems.
  • This review provides a foundation for the development of large-scale CBRAM array systems for future AI applications.