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|Title:||Crystal defect characterization using Python||Authors:||Zayar Naung||Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Zayar Naung (2021). Crystal defect characterization using Python. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150246||Project:||P2042-192||Abstract:||Gallium nitride (GaN) is a mechanically stable wide bandgap, a very hard semiconductor. It has significantly better performance than silicon-based devices such as faster switching speed, lower on-resistance, and higher breakdown strength. Crystals of GaN can be grown on different types of substrates, including silicon carbide (SiC), silicon (Si), and sapphire. The existing manufacturing infrastructure has the low-cost capability to readily leverage large-diameter silicon substrates and grow a GaN epi layer on the surface. The exponential growth of global energy demand and decarbonization has become a pressing issue for semi-con industries to produce high energy-efficient chips while also delivering the performance required. Besides, Covid-19 has also driven the demand for the moon as many people are pushed to use electronic devices. The massive surge in demands for chips has put tremendous pressure on the semiconductor industries to meet the demands without sacrificing quality and reliability. Therefore, semiconductor industries are investing heavily in R&D with the priority to discover new technology-driven solutions so that they can produce efficient chips by using a simpler process integration. A well-integrated process will save time and increase overall wafer fabrications. In this Final Year Project (FYP), Crystal defect characterization using Python will go through methods to rapidly detect the defects on the surface of GaN samples so that we can calculate the surface defect densities, an important metric that can affect transistor performance. This FYP will incorporate state-of-the-art computer vision by the means of python into creating an algorithm that detects the defects on the surface of the GaN samples.||URI:||https://hdl.handle.net/10356/150246||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
Updated on Jan 21, 2022
Updated on Jan 21, 2022
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