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Record W2805887040 · doi:10.3390/mi9060287

3D Integrated Circuit Cooling with Microfluidics

2018· review· en· W2805887040 on OpenAlexaff
Shaoxi Wang, Yue Yin, Chenxia Hu, Pouya Rezai

Bibliographic record

VenueMicromachines · 2018
Typereview
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsYork University
Fundersnot available
KeywordsMicrofluidicsThermal management of electronic devices and systemsThree-dimensional integrated circuitIntegrated circuitThermalElectronicsChipEngineeringMechanical engineeringSystems engineeringNanotechnologyMaterials scienceElectrical engineering

Abstract

fetched live from OpenAlex

Using microfluidic cooling to achieve thermal management of three-dimensional integrated circuits (ICs) is recognized as a promising method of extending Moore law progression in electronic components and systems. Since the U.S. Defense Advanced Research Projects Agency launched Intra/Inter Chip Enhanced Cooling thermal packaging program, the method of using microfluidic cooling in 3D ICs has been under continuous development. This paper presents an analysis of all publications available about the microfluidic cooling technologies used in 3D IC thermal management, and summarized these research works into six categories: cooling structure design, co-design issues, through silicon via (TSV) influence, specific chip applications, thermal models, and non-uniform heating and hotspots. The details of these research works are given, future works are suggested.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.250
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations77
Published2018
Admission routes1
Has abstractyes

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