Papers by Keyword: Diamond Growth

Abstract: Improving diamond growth to achieve high-quality materials for applications like optics and quantum detectors is a key objective. Leveraging machine learning, especially with its challenges such as complex, time-dependent data features and the sheer data volume per growth cycle, presents a promising solution. The extraction of accurate spatial features from images for real-time diamond growth monitoring is challenging due to data's complex and sparse nature. This paper evaluates various feature extraction methods in diamond growth, introducing a novel deep learning approach for precise geometric feature segmentation. It utilizes a human-in-the-loop system for efficient data annotation, significantly cutting down labeling time and costs. Our approach, using the DeeplabV3plus model, showcases high efficiency in feature classification, with accuracies up to 96.31% for pocket holders, 98.60% for diamond tops, and 91.64% for sides, demonstrating deep learning's potential in handling complex dataset features effectively.

Abstract: We have proposed the growth of ultrananocrystalline diamond (UNCD) thin films on p-type (100) silicon etched with 27wt. % KOH in H₂O. To get homogeneous distribution of micro pyramids on the silicon surface we have varied temperature (62 to 77 °C), etching time (1 to 35 min) and exposition diameter area (5 to 18 mm). For UNCD growth we have used hot filament chemical vapor deposition (HFCVD).The gas mixture have used 1 vol.% methane, 9 vol.% hydrogen and 90 vol.% argon, with the total flow rate of 200 sccm, at work pressure of 30 Torr. Images of Scanning Electron Microscopy (SEM) showed UNCD covered the silicon surface following the micro pyramidal morphology. Raman spectra (514.5 nm) showed all feature bands of UNCD such as: transpolyacethylene (1150 cm^-1) and graphite (1350-1575 cm^-1). The X-ray diffraction confirmed Raman spectroscopy. These results showed the silicon micro pyramidal structures obtained at 20 min, 75°C and 10 mm exposition diameter area as the more satisfactory for UNCD growth.