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Record W2634609016 · doi:10.1109/ccece.2017.7946821

Measuring heat transport in electronic devices over small length scales

2017· article· en· W2634609016 on OpenAlexaff
Mohammadreza Shahzadeh, Simone Pisana

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsYork University
Fundersnot available
KeywordsElectronicsTransistorMaterials scienceDissipationThermal conductionThermal management of electronic devices and systemsOptoelectronicsLeakage (economics)Degradation (telecommunications)ThermalJunction temperatureElectronic engineeringComputer scienceElectrical engineeringMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

High current operation is often desirable in electronic devices, as it affects a variety of essential aspects such as switching speed in transistors, sensitivity in sensors, and light output in light-emitting devices. High currents, however, can lead to premature device failure or the degradation of device performance, as the inherent increase in device operating temperature can degrade materials/junctions, increase leakage, or lower mobility. In an effort to characterize the heat transport in electronic devices near the region where the heat is generated and dissipated, we have implemented a frequency-domain thermoreflectance system that allows for the characterization of thermal transport in prototype devices, with the end goal of finding avenues to optimize the heat conduction/dissipation problem. Particularly for emerging devices based on 2D materials such as graphene, there is an opportunity to impact the device performance at the early stages of device development. We present a method that allows for the thermoreflectance signal to be detected at frequencies beyond 50 MHz, which leads to the ability to measure short-range heat transport, where most of the heat dissipation bottlenecks in small scale devices exist.

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 categoriesInsufficient payload (model declined to judge)
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 score0.221
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.244
Teacher spread0.197 · 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
Published2017
Admission routes1
Has abstractyes

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