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Record W2057933381 · doi:10.1116/1.1722404

Fabrication of a self-absorbing, self-supported complementary metal-oxide-semiconductor compatible micromachined bolometer

2004· article· en· W2057933381 on OpenAlexaff
Arbaz Ahmed, R. Niall Tait

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2004
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsBolometerMaterials scienceOptoelectronicsFabricationLayer (electronics)SemiconductorSurface micromachiningSheet resistancePolyimideInfraredOpticsComposite materialDetector

Abstract

fetched live from OpenAlex

This work describes a fabrication process for the manufacturing of a surface micromachined microbridge-type bolometer for infrared thermal imaging applications, which is compatible with any standard complementary metal-oxide-semiconductor process. The bolometer uses a high resistivity amorphous GexSi1−xOy active layer incorporated in a self-absorbing structure for infrared radiation, requiring no additional absorber layer. The device is surface micromachined for thermal isolation and is self-supported by its contact metal legs, with no additional support structure. The design uses a vertical current flow arrangement suitable for a high resistivity a-GexSi1−xOy sensing layer and both sandwich and sandwich-gap configurations are demonstrated. Optimum polyimide sacrificial layer patterning for subsequent deposition of layers is described and optimum metal thickness for the required sheet resistance is studied. A planar surface for the deposited semiconductor layer is obtained by using a recessed base electrode structure. Finally optimum conditions for plasma releasing of the structure are described.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.234
Teacher spread0.225 · 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

Citations12
Published2004
Admission routes1
Has abstractyes

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