2D Materials for Neuron Devices and Neuromorphic Computing

With the rapid development of artificial general intelligence and the energy-efficiency limitations of traditional architectures, bio-inspired neuromorphic computing systems based on brain-like learning offer a promising pathway. Compared to conventional silicon-based devices, 2D materials garner significant attention due to their atomic-scale thickness, tunable optoelectronic properties, and high degree of freedom in heterostructure integration. These exceptional physical characteristics establish 2D materials as strong contenders for neuromorphic hardware. This review systematically introduces 2D material-based artificial neuron devices, summarized across four categories: memristive-type, transistor-type, reconfigurable-type, and optoelectronic-type devices. Next, a development roadmap for biologically inspired neuromorphic systems is summarized, drawing insights from the human brain's learning pathways. Finally, the review discusses future opportunities and challenges for 2D material neuromorphic systems. The evidence indicates that 2D material-based neuromorphic computing systems represent a potential and viable route for future advancements.