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Multifunctional nanomaterials, systems, and algorithms for neuromorphic computing applications: Autonomous systems and wearable robotics
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DOI：10.1016/j.mser.2025.101095.png)
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Abstract
En 中文
Memristive devices are the preferred choice for neuromorphic computer architectures, with low-dimensional materials exhibiting unique functionality resembling biological neurons. The ability to adjust these properties presents significant opportunities for artificial neural networks. This review offers a critical investigation of emerging multi-functional (MF) neuromorphic devices enabled by zero-dimensional, one-dimensional, and two-dimensional materials, van der Waals heterojunctions, and their mechanisms. It highlights the multiple unique bio-inspired device responses that arises from quantum confinement, interfaces, and low-dimensional topology. The advancements, obstacles, and potential solutions for effective neuromorphic computing using low-dimensional MF neuromorphic systems are surveyed. This overview highlights the appealing attributes of neuromorphic computing for future computations and explores the potential for advancing neuromorphic algorithms based on low-dimensional MF systems. The development of low-dimensional MF neural networks for autonomous system applications is outlined. This review article investigates the integration of physical, physiological, and environmental data through low-dimensional MF neural networks, which is essential for wearable robotic applications. It also provides a prospective analysis of the opportunities and challenges associated with low-dimensional MF neuromorphic materials compared to conventional bulk electronic technologies.
Keywords:
memristive devices
neuromorphic computing
low-dimensional materials
van der Waals heterojunctions
bio-inspired systems
Journal
M
IF:
26.8
Papers:
809
Citations:
1.1W
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Cited Papers