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Record W4416610265 · doi:10.1149/ma2025-02351706mtgabs

<i>(Invited)</i> Thermal Transport Engineering in Wide Bandgap Semiconductors

2025· article· W4416610265 on OpenAlexaff
Mark S. Goorsky, Michael E. Liao, Piyush Shah, Brandon Carson, Kaicheng Pan

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsCMC Microsystems (Canada)
Fundersnot available
KeywordsThermal conductivitySemiconductorWaferBand gapSiliconWide-bandgap semiconductorSilicon nitrideLayer (electronics)Lapping

Abstract

fetched live from OpenAlex

Heterogeneous integration via wafer bonding offers potential to incorporate both front-side and back-side cooling for wide and ultrawide bandgap semiconductor device structures. Diamond, silicon carbide, and aluminum nitride can be used as substrates that have superior properties to native substrates (e.g., b-Ga 2 O 3 or GaN) and efforts to produce engineered or composite substrates based on high conductivity substrates with template layers of the desired device material will be addressed with focus on pre-bonded surface chemistry as well as on layer transfer technologies including exfoliation, lapping and polishing, or even spalling. These backside cooling efforts have a counterpoint with the novel idea of using front side layers with high thermal conductivity that are transferred from single crystal or polycrystalline high thermal conductivity substrates.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.223
Teacher spread0.214 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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