As the demand for energy-efficient and adaptive computing architectures continues to surge, conventional von Neumann systems face critical limitations in power consumption and scalability for AI and machine learning workloads. Now, a collaborative team led by Professor Jamal Kazmi (Shanghai University), Professor Peijian Wang (Wenzhou University / National University of Singapore), Professor Mohd Ambri Mohamed (Universiti Kebangsaan Malaysia), Professor Federico Rosei (University of Trieste), Professor Zhiming M. Wang (University of Electronic Science and Technology of China), and Professor Hongwei Song (Jilin University) has presented a comprehensive review on how two-dimensional materials are powering next-generation neuromorphic intelligence.
Why 2D Materials Matter
Traditional digital processors suffer from the von Neumann bottleneck—separating memory and computation creates massive latency and energy costs. Biological systems, by contrast, operate at roughly 20 watts while consuming only 1–100 femtojoules per synaptic event. The introduction of atomically thin 2D materials—including transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), black phosphorus (BP), and emerging tellurene—enables neuromorphic devices with unprecedented control over electronic and optoelectronic properties, bridging the gap between biological efficiency and machine intelligence.
Innovative Material Platforms and Mechanisms
The review systematically maps how distinct 2D material properties enable specific neuromorphic functionalities:
Outstanding Device Performance
The review highlights record-breaking metrics across multiple architectures:
Machine Learning Integration
When paired with neural network algorithms, these devices achieve remarkable results:
Applications and Future Outlook
The review establishes a clear roadmap for 2D neuromorphic systems across six frontiers:
This work establishes a new paradigm for neuromorphic computing, explicitly linking material properties → device architectures → machine learning modes → target applications in a unified framework. By combining atomic-scale thickness with exceptional electronic, optoelectronic, and quantum mechanical properties, 2D materials offer a transformative pathway toward sustainable, adaptive, and intelligent technologies that rival biological neural networks.
Stay tuned for more groundbreaking research from this international collaborative team spanning Shanghai University, Southern University of Science and Technology, Liverpool John Moores University, University of Glasgow, King Fahd University of Petroleum and Minerals, Wenzhou University, National University of Singapore, Universiti Kebangsaan Malaysia, University of Trieste, University of Electronic Science and Technology of China, and Jilin University!
Nano-Micro Letters
News article
2D Materials Powering Neuromorphic Intelligence
23-Jun-2026