Capillary‐Driven Patterned Transfer of MoS 2 Arrays for Gate‐Tunable Optoelectronic Computing

Abstract

ABSTRACT 2D materials have emerged as promising building blocks for next‐generation electronic and optoelectronic devices. The clean transfer of high‐quality, as‐grown 2D materials onto target substrates is a crucial step toward their integration into advanced device architectures. However, the etching‐assisted transfer processes always introduce interfacial contamination and trap states, leading to undesired carrier recombination and degraded optoelectronic performance. Here, we develop a PDMS‐stamp‐assisted large‐scale transfer strategy for the fabrication of high‐quality MoS 2 optoelectronic device arrays. Guided by the work of adhesion theory, a standardized interface engineering approach is established to optimize interfacial adhesion, enabling reliable, high‐yield, and low‐damage transfer. Comprehensive material characterization and optoelectronic measurements confirm the high transfer quality and preserved device performance. A uniform 16 × 16 MoS 2 photodetector with a yield of 98.4% is successfully demonstrated. Finally, we demonstrate a proof‐of‐concept parallel RGB convolutional neural network (CNN) implementation, underscoring its potential in optoelectronic computing.