The advent of high-frequency silicon-based technologies has enabled the design of mixed-signal circuits that incorporate analog, RF, and digital circuit components to build cost-effective system-on-a-chip solutions. Emerging applications provide great incentive for continued scaling of transistor performance, requiring careful attention to mismatch, noise, and reliability concerns. If these mixed-signal technologies are to be employed within space-based electronic systems, they must also demonstrate reliability in radiation-rich environments. SiGe BiCMOS technology in particular is positioned as an excellent candidate to satisfy all of these requirements. The objective of this research is to develop predictive modeling tools that can be used to design new mixed-signal technologies and assess their reliability on Earth and in extreme environments. Ultimately, the goal is to illuminate the interaction of device- and circuit-level reliability mechanisms and establish best practices for modeling these effects in modern circuits. To support this objective, several specific areas have been targeted first, including a TCAD-based approach to identify performance-limiting regions in SiGe HBTs, measurement and modeling of carrier transport parameters that are essential for predictive TCAD, and measurement of device-level single-event transients to better understand the physical origins and implications for device design. These tasks provide the foundation for the bulk of this research, which addresses circuit-level reliability challenges through the application of novel mixed-mode TCAD techniques. All of the individual tasks are tied together by a guiding theme: to develop a holistic understanding of the challenges faced by emerging broadband technologies by coordinating results from material, device, and circuit studies.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/44700 |
| Date | 13 April 2012 |
| Creators | Moen, Kurt Andrew |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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