Design and Process Development of Graphene-Based Geometric Diodes for Enhanced Performance
This thesis advances graphene geometric diodes (GGDs) by addressing fabrication, performance, and design challenges using innovative nanofabrication techniques and artificial intelligence-enhanced computational methodologies.
Overview
Graphene geometric diodes (GGDs) are innovative nanoscale devices that utilize asymmetric structural designs and ballistic transport mechanism to enable rectification without relying on traditional semiconductor junctions. This unique approach allows for directional charge transport, resulting in high-speed and broadband operation, offering promising potential for applications in energy harvesting, terahertz (THz) detection, and high-speed logic circuits. Despite their promise, the field faces significant challenges, including limitations in scalable fabrication, difficulties in achieving improved asymmetry ratios for enhanced performance, and lack of effective geometry design strategies. In addition, graphene's unique properties (particularly its electrical tunability) have not been fully explored in the previous works. This thesis tackles these challenges by advancing the understanding of GGDs and presenting innovative solutions through advanced nanofabrication approaches and computational methodologies, including artificial intelligence (AI).
Presenters
Brief Biography
Heng Wang is a Ph.D. Candidate in Electrical and Computer Engineering of King Abdullah University of Science and Technology (KAUST), where he specializes in nanofabrication, lithography, and nanodevice processing. He earned his bachelor's degree in Microelectronic Science and Engineering from the University of Electronic Science and Technology of China (UESTC). During his academic career, he developed strong hands-on experience in cleanroom processes and supported collaborative research in advanced electronics and quantum devices.
