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Record W4385066962 · doi:10.3390/electronics12143154

Advanced Thermal Control Using Chip Cooling Laminate Chip (CCLC) with Finite Element Method for System-in-Package (SiP) Technology

2023· article· en· W4385066962 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.

Bibliographic record

VenueElectronics · 2023
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsUniversité de SherbrookeUniversité du Québec en Outaouais
Fundersnot available
KeywordsMultiphysicsFinite element methodSystem in packageChipMechanical engineeringReliability (semiconductor)ThermalElectronic packagingElectronic engineeringMaterials scienceTemperature controlComputer scienceEngineeringElectrical engineeringStructural engineering

Abstract

fetched live from OpenAlex

This paper introduces a novel approach to address thermal management challenges in system-in-package (SiP) technology, which is a significant concern in various advanced technologies. The main objective is to assess the electrical and thermal performance of the SiP model by utilizing Chip Cooling Laminate Chip (CCLC) technology. To achieve this, we employed finite element method (FEM) analysis using COMSOL Multiphysics® and MATLAB® to compare the results of electrical and thermal SiP models with and without CCLC technology. The numerical simulations revealed that, as opposed to the traditional model, the temperature variation decreased significantly with a uniform temperature distribution when employing the CCLC technology. Additionally, the thermal conduction performance of the packaging system using CCLC demonstrated remarkable reliability and resolution with cost-effective micro-devices, particularly in micro-medicine applications. The analysis of the electrical and thermal models reported a maximum error between them of 1.15 ∘C.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.245
Teacher spread0.237 · 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