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Record W7042883457

Reconfigurable Impedance Matching Networks Based on RF-MEMS and CMOS-MEMS Technologies

2010· dissertation· en· W7042883457 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2010
Typedissertation
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooGeorgia Institute of Technology
KeywordsCapacitorFilter (signal processing)Noise (video)Electrical impedanceQuality (philosophy)Interrupter
DOInot available

Abstract

fetched live from OpenAlex

Reconfigurable impedance matching networks are an integral part of multiband radio-frequency (RF) transceivers. They are used to compensate for the input/output impedance variations between the different blocks caused by switching the frequency band of operation or by adjusting the output power level. Various tuning techniques have been developed to construct tunable impedance matching networks employing solid-state p-i-n diodes and varactors. At millimeter-wave frequencies, the increased loss due to the low quality factor of the solid-state devices becomes an important issue. Another drawback of the solid-state tuning elements is the increased nonlinearity and noise at higher RF power levels. \n \nThe objective of the research described in this thesis is to investigate the feasibility of using RF microelectromechanical systems (RF-MEMS) technology to develop reconfigurable impedance matching networks. Different types of tunable impedance matching networks with improved impedance tuning range, power handling capability, and lower insertion loss have been developed. Another objective is to investigate the realization of a fully integrated one-chip solution by integrating MEMS devices in standard processes used for RF integrated circuits (RFICs). \n \nA new CMOS-MEMS post-processing technique has been developed that allows the integration of tunable RF MEMS devices with vertical actuation within a CMOS chip. Various types of CMOS-MEMS components used as tuning elements in reconfigurable RF transceivers have been developed. These include tunable parallel-plate capacitors that outperform the available CMOS solid-state varactors in terms of quality factor and linearity. A tunable microwave band-pass filter has been demonstrated by employing the proposed RF MEMS tunable capacitors. For the first time, CMOS-MEMS capacitive type switches for microwave and millimeter-wave applications have been developed using TSMC 0.35-µm CMOS process employing the proposed CMOS-MEMS integration technique. The switch demonstrates an excellent RF performance from 10-20 GHz. \n \nNovel MEMS-based reconfigurable impedance matching networks integrated in standard CMOS technologies are also presented. An 8-bit reconfigurable impedance matching network based on the distributed MEMS transmission line (DMTL) concept operating at 13-24 GHz is presented. The network is implemented using standard \n0.35-µm CMOS technology and employs a novel suspended slow-wave structure on \na silicon substrate. To our knowledge, this is the first implementation of a DMTL tunable MEMS \nimpedance matching network using a standard CMOS technology. A reconfigurable \namplifier chip for WLAN applications operating at 5.2 GHz is also designed and implemented. The amplifier achieves maximum power gain under variable load and \nsource impedance conditions by using the integrated RF-MEMS impedance \nmatching networks. This is the first single-chip implementation of \na reconfigurable amplifier using high-Q MEMS impedance matching networks. \nThe monolithic CMOS implementation of the proposed RF MEMS impedance matching networks enables the development of future low-cost single-chip RF multiband transceivers with improved performance and functionality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.169
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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