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Record W1492529677 · doi:10.1109/eptc.2005.1614461

TIM Characterization for High Performance Microprocessors

2006· article· en· W1492529677 on OpenAlexaff
Liu Min, Y.C. Mui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsCharacterization (materials science)Computer scienceComputer architectureMaterials science

Abstract

fetched live from OpenAlex

TIM characterization is critical for the thermal solutions of higher power IC chips, like microprocessors. This paper firstly reviews the interface resistance between two contacting solids, thermal interface materials and standard test methodology based on ASTM standard. It shows that the ASTM standard testers can only find out the pure material properties, like heat conductivity of the TIMs under ideal conditions. However, the TIM performance are affected by many factors and thus TIM should be evaluated under specified application conditions. Thus, a TIM characterization system for microprocessor applications is proposed and demonstrated in detail in this paper. The set up can cater all the important factors, such as clamping force; roughness and flatness of the contacting surfaces; the heating area and the path of the heat transfer, etc. Several TIMs were characterized for both lidded and lidless packages. It is found that the process of the TIM preparation and attachment can affect the performance significantly. Inappropriate attachment of solder A leads to an overall thermal resistance even higher than that of dry contact. The area of the contact surface also plays an important role. For lidded packages, the larger contacting surface makes the overall thermal resistance less sensitive to the TIM used. The measurement of the TIMs under the actual conditions can thus be used as the thermal criteria for the TIM selection. The impact of the clamping force and the number of insertions has also been investigated and quantified under the actual application conditions. Based on the current studies, the TIM preparation process and the application parameters can be optimized to achieve better thermal performance and longer life span. Although the present set up was for mainly for microprocessor packages, the concept and methodology can be implemented to the applications of other IC chips. Finally, the criteria for TIM selections are discussed and it is recommended that TIM be selected on the evaluation of all the important factors.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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