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Record W82273590 · doi:10.1007/1-4020-3013-4_30

Silicon-on-Insulator Substrates with Buried Ground Planes (GPSOI)

2005· book-chapter· en· W82273590 on OpenAlexaff
Michael Bain, S. Stefanos, Paul Baine, Siu Hong Loh, Michael Jin, J.H. Montgomery, B.M. Armstrong, H.S. Gamble, J.S. Hamel, David McNeill, Michaël Kraft, H.A. Kemhadjian

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSilicon on insulatorMaterials scienceGround planeOptoelectronicsWaferSiliconCapacitorSubstrate (aquarium)DielectricElectronic engineeringLayer (electronics)Electrical engineeringElectronic circuitIntegrated circuitSilicideEngineeringNanotechnologyVoltage

Abstract

fetched live from OpenAlex

Advanced integrated circuits may employ SOI substrates and incorporate both analogue and digital systems on a single chip. These system-on-chip integrated circuits are susceptible to cross talk noise generated by the digital components. This paper addresses the issue and describes an SOI substrate produced by wafer bonding which incorporates a tungsten silicide ground plane layer. This ground plane layer suppresses the cross talk yielding a20 dB improvement in performance compared with alternative techniques. Double gate MOS capacitor structures have been manufactured on these GPSOI substrates and the overlying silicon layer has been shown to be of high quality, unaffected by the underlying silicide. The buried insulator layer incorporates undoped polysilicon which has been shown to act as a dielectric layer.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.006

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 head0.191
Teacher spread0.177 · 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
GenreMethods

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

Citations1
Published2005
Admission routes1
Has abstractyes

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