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Record W1850307177 · doi:10.1109/ccece.2005.1556941

Low loss inductors built-on pecvd intrinsic amorphous silicon for rf integrated circuits

2006· article· en· W1850307177 on OpenAlexafffund
S. Chang, S. Sivoththaman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInductorMaterials sciencePlasma-enhanced chemical vapor depositionOptoelectronicsAmorphous solidSubstrate (aquarium)CMOSRFICRadio frequencyAmorphous siliconSiliconElectrical resistivity and conductivityElectronic engineeringElectrical engineeringChemistryCrystalline siliconEngineering

Abstract

fetched live from OpenAlex

The Q of inductors on Si is limited by the series resistance of the metal at low frequency and the substrate resistivity at high frequency. Oxide is generally used to isolate the useful signal of the inductor from the lossy substrate. However, stoichiometric SiO2is processed at high temperature which eliminates the possibility of post-CMOS integration. PECVD amorphous Si can be deposited at low temperature, is easily integrated with most Si-based processes, and intrinsic a-Si:H displays low conductivity. In this work, we show that i-a-Si:H deposited at low temperature (250degC) is used in a novel approach as the isolation material for planar inductors on Si for RFICs. More than 50% improvement in Q was measured when 1.5 mum i-a-Si:H film is deposited on the Si substrate prior to fabricating the inductor. This justifies the influence of i-a-Si:H on the RF performance of an inductor. Intrinsic a-Si: H proves to be a promising material for the isolation of RF devices on low resistivity Si

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: Empirical
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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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

Citations2
Published2006
Admission routes2
Has abstractyes

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