Temperature control and characterization of silicon-germanium growth by rapid thermal chemical vapor deposition

dc.contributor.advisorEdgar, Thomas F.en
dc.contributor.advisorBanerjee, Sanjayen
dc.creatorHwang, Sung-Bo, 1965-en
dc.date.accessioned2011-04-25T21:42:36Zen
dc.date.available2011-04-25T21:42:36Zen
dc.date.issued2002-05en
dc.descriptiontexten
dc.description.abstractRapid thermal chemical vapor deposition (RTCVD) is an emerging technology to utilize low thermal budgets required to grow silicon-germanium alloys in a coherent way. However, the current state-of-the-art in RTCVD technique lacks some key elements required for acceptance of RTCVD in mainstream IC fabrication. These shortcomings include adequate control of wafer temperature during processing, and sufficient understanding of the growth kinetics. This dissertation describes and discusses the temperature control in RTCVD, the growth, and characterization of silicon-germanium alloys. The RTCVD system provides very reliable temperature-measurements, for a range of 480~820°C, based on infrared-light (1.3 or 1.55µm) absorption in the silicon wafer during the growth of silicon-germanium alloys. A wafer heat transfer model developed using the view-factor analysis is used to investigate temperature distributions with respect to lamp configurations in RTCVD system. For a precise temperature control, a neural model-based controller in single-input-single-output (SISO) system is proposed, and compared with other controllers. Silicongermanium alloys, in various semiconductor structures including dots, have been grown by RTCVD where temperature is well-controlled by the model-based controller. The structural and chemical properties of silicon-germanium alloys are characterized by X-ray diffraction, atomic force microscopy (AFM), transmission electron microscopy (TEM), and secondary ion mass spectrometry (SIMS). The different growth characteristics dominated by a silicon-source gas are exploited, and their process models are developed with the experimental data utilizing neural networks employed the Bayesian framework to accurately describe the process behaviors such as growth rate and Ge fraction in alloys with respect to process variables (to capture the process nonlinearity). By controlling growth rate and Ge fraction, a uniform and a grading Ge profile in silicon-germanium layers are demonstrated for a device fabrication. In addition, a substrate dependence of growth mechanism is utilized to form dots on dielectric materials including high k materials.
dc.description.departmentChemical Engineeringen
dc.format.mediumelectronicen
dc.identifier.urihttp://hdl.handle.net/2152/11012en
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.rights.restrictionRestricteden
dc.subjectGermaniumen
dc.subjectSiliconen
dc.subjectVapor-platingen
dc.titleTemperature control and characterization of silicon-germanium growth by rapid thermal chemical vapor depositionen
thesis.degree.departmentChemical Engineeringen
thesis.degree.disciplineChemical Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen

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