AI-assisted wafer-scale exfoliation and transfer of 2D materials: status, challenges and perspectives

Abstract

Moving two-dimensional (2D) materials from lab to industry requires breakthroughs in scalable exfoliation and transfer methods. While traditional mechanical exfoliation methods can produce high-quality flakes, they suffer from poor reproducibility and low yield. In recent years, metal-assisted exfoliation techniques have significantly improved monolayer yield and structural uniformity. Furthermore, scalable transfer strategies such as polyvinyl alcohol-assisted transfer and van der Waals integration have achieved cleaner interfaces and higher alignment accuracy. However, manual operation remains a major limitation to consistency and efficiency. Artificial intelligence (AI) is emerging as a transformative tool, enabling intelligent control of the exfoliation and transfer process through real-time parameter optimization, crack prevention, and path planning. Deep learning architectures facilitate layer identification and defect detection, while reinforcement learning enables high-precision autonomous robotic manipulation. This article systematically reviews the latest advances in the field of 2D material exfoliation and transfer, highlighting the important role of AI in addressing core process bottlenecks and enabling the scalable, reliable, and automated fabrication of 2D materials.