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Record W2116408652 · doi:10.1109/mnrc.2008.4683413

A pixel-by-pixel thermal conductance tuning mechanism for uncooled microbolometers

2008· article· en· W2116408652 on OpenAlexaff
Nezih Topaloğlu, Patricia Nieva, Mustafa Yavuz, Jan P. Huissoon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrobolometerMaterials scienceOptoelectronicsDetectorSubstrate (aquarium)Dot pitchPixelVoltageAtmospheric temperature rangeOperating temperatureThermalBolometerBiasingOpticsElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

The increase in the demand of using infrared detectors for thermal imaging of high temperature scenes initiated the research in microbolometer arrays with high operation temperature range. An efficient way of increasing this range is tuning the thermal conductance of the microbolometer array by electrostatic actuation, which is achieved by applying an actuation voltage to the substrate. However, using the substrate for actuation does not support pixel-by-pixel actuation, limiting the capabilities of the tunability. In this research, we demonstrate applying the actuation voltage to the micromirror which is located below the microbolometer. To avoid contact of the microbolometer to the micromirror, stoppers are used. We report that the thermal conductance is doubled at an actuation voltage of 12 volts, making it an efficient mechanism that can be used at next generation adaptive microbolometers.

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 score0.002

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.237
Teacher spread0.209 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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