An AI-driven robotic system for two-dimensional hetero-assemblies

TL;DR

This paper introduces an AI-driven automated system for fabricating two-dimensional heterostructures, enabling scalable, high-precision assembly of nanomaterials like twisted bilayer graphene, and demonstrating its effectiveness through superconductivity experiments.

Contribution

The authors develop an intelligent automation system utilizing reinforcement learning for the scalable and precise assembly of 2D heterostructures, advancing current manual and inefficient methods.

Findings

Successfully fabricated twisted bilayer graphene exhibiting superconductivity near the magic angle.

Demonstrated the system's ability to improve performance through reinforcement learning.

Paved the way for high-throughput, AI-assisted nanomaterial fabrication.

Abstract

Nanomaterials stacked on-demand, such as rotationally assembled two-dimensional (2D) van der Waals (vdW) layered compounds, provides a versatile platform for quantum simulation and the exploration of exotic electronic phases. Currently, however, such nanoassemblies remain largely confined to inefficiency, manually operated process, limiting their potential for probing emergent physical phenomena. There is a pressing need in the field for high-precision, automated assembling techniques, especially for the scalable fabrication of 2D twistronic heterostructures. Here, we present an intelligent automation system dedicated to the fabrication of van der Waals stacks, following the state-of-the-art protocol for dry transfer of exfoliated 2D materials. The system further employs metadata generated from each automated stacking procedure to perform reinforcement learning, thereby continuously bettering its performances. As a concrete demonstration, we fabricate twisted bilayer graphene (TBLG) -- known for its challenging preparation -- and exhibit its unconventional superconductivity near the magic angle. Our work may pave the way for high-throughput fabrication of low-dimensional nanomaterials including twistronic heterostructures, where integrating data mining and artificial intelligence can accelerate the discovery of novel physical phenomena.