High-Throughput Identification and Statistical Analysis of Atomically Thin Semiconductors

Abstract

Transition metal dichalcogenides (TMDs) are layered two-dimensional semiconductors explored for various optoelectronic applications, ranging from light-emitting diodes to single-photon emitters. To interact strongly with light, such devices require monolayer TMDs, which exhibit a direct bandgap. These atomically thin sheets are typically obtained through mechanical exfoliation followed by manual identification with a brightfield optical microscope. While this traditional procedure provides high-quality crystals, the identification step is time-intensive, low-throughput, and prone to human error, creating a significant bottleneck for TMD research. Here, we report a simple and fully automated approach for high-throughput identification of TMD monolayers using photoluminescence microscopy. Compared to a manual search and verification, our methodology offers a four-orders-of-magnitude decrease in the time a researcher must invest per identified monolayer. This ability enables us to measure geometric and photoluminescence-intensity features of more than 2,400 monolayers and bilayers of WSe$_2$, MoSe$_2$, and MoS$_2$. Due to these large numbers, we can study and quantify material properties previously inaccessible. For example, we show that the mean photoluminescence intensity from a monolayer correlates with its size due to reduced emission from its edges. Further, we observe large variations in brightness (up to 10$\times$) from WSe$_2$ monolayers of different batches produced by the same supplier. Therefore, our automated approach not only increases fabrication efficiency but also enhances sample quality for optoelectronic devices of atomically thin semiconductors.