A Novel Focused Local Learning Wavelet Network with Application to In Situ Monitoring During Selective Silicon Epitaxy

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Title: A Novel Focused Local Learning Wavelet Network with Application to In Situ Monitoring During Selective Silicon Epitaxy
Author: Rying, Eric Arne
Advisors: Dr. Griff L. Bilbro, Chair
Dr. F. Yates Sorrell, Co-Chair
Dr. Mehmet C. Ozturk, Member
Dr. J.C. Lu, Member
Dr. Winser E. Alexander, Member
Dr. Wesley E. Snyder, Member
Abstract: This dissertation reports results which pioneered the novel application of wavelets to processes and issues critical to semiconductor manufacturing. This work is especially germane given: 1) the rising costs and complexity of manufacturing; 2) the increasing deluge of data provided by process andequipment sensors that are developed, in part, to address these costs; and 3) the lack of adequate tools for handling the information overload. The sheer quantity of data is currently outstripping conventional means of storage and analysis; as a result, an increased fraction of this data must be discarded or incompletely processed. Ultimately, by developing advanced tools and methodologies to address these issues, cost and performance-driven decisions can be made in a timely and cost-effective manner. The content of this dissertation impacts these critical issues through the following specific contributions. Wavelet-based methodologies were developed for the localized modeling and compression of key process-relevant information, especially information pertaining to the detection of equipment and process faults. These methodologies significantly contribute to the following general problems: 1) identification of specific local features in potentially large nonstationary data sets; 2) compression of entire data sets consisting of disjoint smooth and nonsmooth segments; and 3) improvement in the quality of modeling for important local features carrying key information. This thesis addressed these problems using wavelet networks, as the tool, and the following novel approaches: 1) a novel objective function that incorporates both global and local error as well as model parsimony; 2) a novel adaptive network initialization scheme; 3) a novel application of the wavelet transform modulus maxima (WTMM) representation to help determine and prioritize the set of local features; 4) a new network construction procedure; and 5) a new errorspace analysis (ESA) technique, to assist in the visualization of both local and global network approximation errors during wavelet network construction. Information gathered in situ using a quadrupole mass spectrometer (QMS) sensor facilitated the development of a novel and unique approach for monitoring the selective film thickness during selective growth of silicon epitaxy. Moreover, this approach is applicable to the detection of one particularly critical process fault --- loss of selectivity. In particular, since QMS sensors are currently in widespread use in most vacuum systems, this technique represents a viable, cost-effective solution to selectivity loss detection that appears readily transferable to other process chemistries. In general, by correlating similar in situ process metrics (e.g., signal area) to ex situ process observables (e.g., thin-film thickness), contributions are made to: 1) thin-film metrology and metal oxide semiconductor field effect transistor (MOSFET) gate-stack engineering; 2) run-to-run detection of aberrant signalmodes, including process and equipment faults; and 3) decision-making using wavelet-compressed spaces. The methodologies developed in this thesis appear to be broadly applicable to otherdisciplines and fields of endeavor, including intelligent manufacturing and information technology.
Date: 2001-04-10
Degree: PhD
Discipline: Electrical Engineering
URI: http://www.lib.ncsu.edu/resolver/1840.16/3746

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