Proceedings Article | 10 April 2024
KEYWORDS: Image segmentation, 2D materials, Scanning electron microscopy, Image processing, Semiconducting wafers, Machine learning, Algorithm development, Image processing algorithms and systems, Atomic force microscopy, Tungsten
2D materials hold significant potential for enhancing semiconductor device performance. However, their integration necessitates the establishment of a robust metrology that is both accurate and fast, enabling a comprehensive understanding and precise control of their growth processes. Scanning electron microscopy (SEM) ticks all the boxes to be a promising technique for 2D materials characterization due to its sensitivity to monolayers (ML) and its high measurement throughput. Nevertheless, automating its measurement analysis is essential to avoid slow data processing and inaccuracies in results. In this work, I propose a set of image-processing algorithms to extract various 2D material properties from SEM images and reveal hidden aspects of their growth processes. Firstly, I implemented and benchmarked two competing segmentation algorithms to process raw SEM images and localize surface regions corresponding to the substrate, 1ML, and 2ML. The first algorithm employs a statistical approach, named global thresholding, while the second is based on supervised machine Learning. These two algorithms were evaluated on a batch of wafers where tungsten disulfide (WS2) material was grown, reaching a maximum of 2ML. The machine learning algorithm demonstrated exceptional performance, achieving segmentation success rates that surpassed 98%, outperforming the global thresholding technique, which has a success rate of 86.5%. Subsequently, other algorithms were developed to extract quality indicators of 2D material layers from the segmented images, such as the coverage rate, and the count of basic crystals and islands. Reviewing all extracted properties enabled us to assess the process uniformity on wafers across different facets. On the other hand, the cross-analysis of these features unveiled some fundamental properties of the studied growth process. We determined the 1ML coverage at which crystals transition from a quasi-isolated growth to the coalescence phase, as well as when the formation of a uniform 1ML begins. Finally, we deduced the important relationship between the growth rates of 1ML and 2ML.
