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2D Materials for Neuron Devices and Neuromorphic Computing.
Chenyu Ye1, Yihan Liu1, Tao Zeng1
1State Key Laboratory of Integrated Chips and Systems, College of Integrated Circuits and Micro-Nano Electronics, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
Small (Weinheim an Der Bergstrasse, Germany)
|January 5, 2026
Summary
2D materials offer a promising pathway for developing energy-efficient artificial general intelligence (AGI) hardware. This review explores 2D material-based artificial neuron devices and outlines a roadmap for future neuromorphic computing systems.
Area of Science:
- Materials Science
- Computer Science
- Neuroscience
Background:
- Rapid advancements in artificial general intelligence (AGI) highlight the need for energy-efficient computing architectures.
- Traditional silicon-based architectures face energy-efficiency limitations.
- Bio-inspired neuromorphic computing systems, mimicking brain-like learning, present a promising alternative.
Purpose of the Study:
- To systematically review 2D material-based artificial neuron devices for neuromorphic computing.
- To outline a development roadmap for bio-inspired neuromorphic systems.
- To discuss future opportunities and challenges in 2D material neuromorphic systems.
Main Methods:
- Categorization of 2D material-based artificial neuron devices into memristive-type, transistor-type, reconfigurable-type, and optoelectronic-type.
- Summarization of a development roadmap for neuromorphic systems inspired by human brain learning pathways.
- Discussion of future prospects and hurdles for 2D material neuromorphic systems.
Main Results:
- 2D materials possess unique properties (atomic thickness, tunable optoelectronics, heterostructure integration) making them suitable for neuromorphic hardware.
- A comprehensive overview of different types of 2D material-based artificial neuron devices is presented.
- A roadmap for developing biologically inspired neuromorphic systems is proposed.
Conclusions:
- 2D materials are strong contenders for next-generation neuromorphic hardware due to their exceptional physical characteristics.
- 2D material-based neuromorphic computing systems offer a viable and potential route for future advancements in AGI.
- Further research and development are needed to overcome challenges and realize the full potential of these systems.
