2D Materials Powering Neuromorphic Intelligence.

Authors

Jamal Kazmi, Waqas Ahmad, Muhammad Naqi, Yawar Abbas, Aumber Abbas, Peijian Wang, Mohd Ambri Mohamed, Federico Rosei, Zhiming M Wang, Hongwei Song

Published in

Nano-micro letters. Volume 18. Issue 1. Jun 24, 2026. Epub Jun 24, 2026.

Abstract

The exponential demand for energy-efficient and adaptive computing architectures drives the evolution of artificial intelligence (AI) and machine learning (ML). Neuromorphic computing, inspired by biological neural networks, overcomes the limitations of traditional von Neumann architectures, including high energy consumption and limited scalability. The introduction of two-dimensional (2D) materials, such as transition metal dichalcogenides, hexagonal boron nitride, black phosphorus, and tellurene, enables neuromorphic devices with unprecedented control over electronic and optoelectronic properties. These materials exhibit atomic-scale thickness, high carrier mobility, and tunable bandgaps, facilitating synaptic behaviours such as spike-timing-dependent plasticity and paired-pulse facilitation. This review describes the integration of 2D materials into neuromorphic systems, highlighting applications in wearable electronics, brain-machine interfaces, and quantum neuromorphic platforms. In wearable and edge computing, 2D-based devices enable localized, ultra-low-power data processing. In brain-machine interfaces, they enhance signal transduction and neural interfacing. Quantum effects in 2D materials further enable hybrid quantum-classical neuromorphic architectures for high-dimensional computational tasks. Despite significant advances, challenges in reproducibility, scalability, and stability remain. Addressing these limitations through innovations in synthesis and defect passivation is essential for practical application. This review underscores the transformative potential of 2D-material-based neuromorphic computing for energy-efficient AI. Integration of 2D materials into neuromorphic computing architectures offers a promising pathway toward energy-efficient and adaptive systems that bridge biological learning mechanisms with machine intelligence.

PMID:
42340555
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.

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