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Resistive Switching Oxides: Mechanism, Performance, and Device-Algorithm Co-Design for Artificial Intelligence.

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Summary
This summary is machine-generated.

Complex oxides show promise for brain-inspired computing due to their resistive switching properties. This review explores their mechanisms, applications in neuromorphic devices, and future development directions.

Keywords:
artificial synapses and neuronsdevice‐algorithm co‐designneuromorphic computingoxidesresistive switching

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • The human brain's efficiency in learning and processing inspires new computing paradigms.
  • Brain-inspired hardware offers superior energy efficiency and parallel processing.
  • Complex oxides possess unique metastable states enabling resistive switching.

Purpose of the Study:

  • To review recent advances in resistive switching of complex oxides for brain-inspired computing.
  • To focus on physical mechanisms and device-algorithm co-design.
  • To explore biomimetic applications and future outlook.

Main Methods:

  • Elucidation of materials science and multiscale mechanisms of resistive switching in complex oxides.
  • Discussion of resistive switching behaviors and performance of complex oxide devices.
  • Exploration of device-level biomimetic applications and circuit-algorithm co-designs.

Main Results:

  • Complex oxides exhibit diverse resistive switching dynamics due to metastable states.
  • Single devices can achieve bioinspired behaviors, with multidevice integration enabling brain-like functions.
  • State-of-the-art performance and device-level biomimetic applications are presented.

Conclusions:

  • Complex oxides are key materials for developing efficient neuromorphic devices.
  • Device-algorithm co-design is crucial for realizing advanced brain-inspired computing.
  • Further research is needed to overcome current challenges and advance complex oxide neuromorphic devices.