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Record W2806161090 · doi:10.1109/tvlsi.2018.2834499

Electronics and Packaging Intended for Emerging Harsh Environment Applications: A Review

2018· review· en· W2806161090 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2018
Typereview
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaAirbus
KeywordsElectronicsAutomotive industrySilicon carbideReliability (semiconductor)Engineering physicsAerospaceGallium nitrideMaterials scienceComputer scienceElectrical engineeringSystems engineeringNanotechnologyEngineeringAerospace engineeringPower (physics)

Abstract

fetched live from OpenAlex

Several industrial applications require specific electronic systems installed in harsh environments to perform measurements, monitoring, and control tasks such as in space exploration, aerospace missions, automotive industries, down-hole oil and gas industry, and geothermal power plants. The extreme environment could be surrounding high-, low-, and wide-range temperature, intense radiation, or even a combination of above conditions. We review, in this paper, the main leading applications that demand advanced technologies to fit the unconventional requirements of extreme operating conditions, discussing their main merits and limits compared to established and emerging technologies in this field, including silicon (Si), silicon on insulator (SOI), silicon germanium (SiGe), silicon carbide (SiC) as well as III–V semiconductors particularly the gallium nitride (GaN) semiconductor. In spite of successfully exceeding extreme conditions borders by developing advanced semiconductor devices dedicated for harsh environments, especially in high-temperature applications, the packaging challenges are still limiting the reliability of the developed technologies. Those challenges are examined in this review in terms of limitations and proposed solutions.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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