No abstract

Controllable and reproducible synthesis of 2D materials is crucial for their future applications. Chemical vapor deposition (CVD) promises scalable and high‐quality growth of 2D materials. However, to optimize CVD growth, multiple parameters have to be carefully selected. Design of experiments (DoE) is a consistent and versatile tool to optimize all parameters simultaneously in a controlled way. This study exploits DoE statistical approaches to show how the CVD growth of transition metal dichalcogenides (TMDs) can be optimized, using tungsten disulfide as an example. A designed set of 29 different processes is used to cover the entire parameter space. The resulting growth output is characterized in terms of material morphology for factors such as single crystal size and continuous film size. The nonlinear model used to fit the output as a function of input parameters provides crucial insights into the nontrivial CVD process ensuring easy and systematic growth optimization. The predicted processes show successful optimization with respect to both the resulting material and the process stability. This powerful technique can be adapted for different setups and other TMD materials.