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Operando microscopy for neuromorphic hardware

delete2026-06-17
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PRE
AI
Y
Yimei Zhu *
A
Alex Frañó
S
Shriram Ramanathan
DOI:10.1038/s41563-026-02629-zdelete
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Abstract

Abstract

En 中文
Microscopy techniques can uncover the physical properties and dynamic behaviours of materials, driving the discovery of emergent phenomena and guiding the design of next-generation computing hardware. As artificial intelligence becomes pervasive, the demand for high-performance materials to support sustainable information technologies is growing. This Review highlights state-of-the-art imaging from electron and X-ray to optical techniques to probe the dynamics of neuromorphic materials, including operando characterization of devices. We examine design principles for neuromorphic materials, along with obstacles that hinder their development. Emphasis is placed on spatially and temporally resolved approaches that capture state changes including phase transitions, ferroic switching and spin-wave propagation that emulate biological components such as neurons, synapses and their connectivity. We discuss challenges in operando characterization and the integration of artificial intelligence-driven analysis for feedback-guided material discovery. Finally, we outline opportunities for real-time imaging of neuromorphic systems, paving the way towards adaptive, brain-inspired hardware. This Review highlights state-of-the-art imaging from electron and X-ray to optical techniques to probe the dynamics of neuromorphic materials, including operando characterization of devices.

Journal

Nature Materials cover
Nature Materials
IF:
38.5
Papers:
6.8K
Citations:
11.5W

Organization

B
brookhaven national laboratory
Scholars:
555
Papers: 238
Citations: 0
U
university of california san diego
Scholars:
5.0K
Papers: 2.3K
Citations: 1
R
rutgers, the state university of new jersey
Scholars:
152
Papers: 62
Citations: 0
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