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  3. Operando Microscopy For Neuromorphic Hardware.
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  3. Operando Microscopy For Neuromorphic Hardware.

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Operando microscopy for neuromorphic hardware.

Yimei Zhu1, Alex Frano2, Shriram Ramanathan3

  • 1Brookhaven National Laboratory, Upton, NY, USA. zhu@bnl.gov.

Nature Materials
|June 17, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This review explores advanced microscopy techniques for understanding neuromorphic materials, crucial for developing brain-inspired computing hardware and sustainable information technologies.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Nanotechnology

Background:

  • Growing demand for high-performance materials in artificial intelligence and sustainable computing.
  • Microscopy is key to understanding material properties and dynamic behaviors for next-generation hardware.
  • Neuromorphic materials offer potential for emulating biological neural networks.

Purpose of the Study:

  • To review state-of-the-art microscopy techniques for probing neuromorphic material dynamics.
  • To examine design principles and challenges in neuromorphic material development.
  • To highlight the role of operando characterization and AI in material discovery.

Main Methods:

  • Electron microscopy
  • X-ray imaging
  • Optical microscopy techniques
  • Operando characterization of devices

    • Main Results:

    • Spatially and temporally resolved imaging captures critical state changes (phase transitions, ferroic switching, spin-wave propagation).
    • These dynamics emulate biological neuronal functions like synapses and connectivity.
    • Challenges in operando characterization and AI integration for material discovery are identified.

    Conclusions:

    • Advanced imaging techniques are vital for understanding and designing neuromorphic materials.
    • Overcoming characterization challenges and integrating AI can accelerate feedback-guided material discovery.
    • Real-time imaging of neuromorphic systems will enable adaptive, brain-inspired hardware.