Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning

Chemical vapor deposition of hexagonal boron nitride on germanium from borazine

The growth of hexagonal boron nitride (hBN) directly onto semiconducting substrates, like Ge and Ge on Si, promises to advance the integration of hBN into microelectronics. However, a detailed understanding of the growth and characteristics of hBN islands and monolayers on these substrates is lacking. Here, we present the growth of hBN on Ge and Ge epilayers on Si via high-vacuum chemical vapor deposition from borazine and study the effects of Ge sublimation, surface orientation, and vicinality on the shape and alignment of hBN islands. We find that suppressing Ge sublimation is essential for growing high quality hBN and that the Ge surface orientation and vicinality strongly affect hBN alignment. Interestingly, 95% of hBN islands are unidirectionally aligned on Ge(111), which may be a path toward metal- and transfer-free, single-crystalline hBN. Finally, we extend the growth time and borazine partial pressure to grow monolayer hBN on Ge and Ge epilayers on Si. These findings provide new insights into the growth of high-quality hBN on semiconducting substrates.

When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.