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Record W2149175241 · doi:10.1109/itherm.2010.5501252

Thermal characterization of planar interconnect architectures under different rapid transient currents using the transmission line matrix and finite element methods

2010· article· en· W2149175241 on OpenAlexaff
Banafsheh Barabadi, Yogendra Joshi, Satish Kumar, Gamal Refai-Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCopper Interconnects and Reliability
Canadian institutionsAdvanced Micro Devices (Canada)
FundersSemiconductor Research Corporation
KeywordsTransient (computer programming)Transmission lineMicroelectronicsResistorInterconnectionJoule heatingFinite element methodMaterials scienceTransmission-line matrix methodMatrix (chemical analysis)Reliability (semiconductor)Electronic engineeringPlanarElectromigrationJoule effectTopology (electrical circuits)Computational physicsComputer sciencePhysicsElectrical engineeringOptoelectronicsEngineeringVoltageComputational electromagneticsThermodynamicsTelecommunicationsComposite material

Abstract

fetched live from OpenAlex

Considering the increasing level of integration and high current densities, the quality and reliability of interconnects in microelectronics is a major challenge. This work studied the problem of transient Joule heating in 180 nm by 360 nm interconnects in a two-dimensional (2D) inhomogeneous model. Specifically, the effects of the duration and amplitude of rapid square-wave source current pulses (100 ns and 1 μs) were investigated. The transmission line matrix (TLM) method was implemented with the link-resistor (LR) formulation and the results were compared with a finite element (FE) model that was developed in LS-DYNA®. This comparison showed that the overall behavior of the TLM models were in good agreement with the corresponding FE models while, near the heat source, the transient TLM solutions developed slower than the FE solutions. Overall, computational efficiency of the TLM method and its ability to accept non-uniform 2D and 3D mesh and variable time-step make it a good candidate for multi-scale analysis of Joule heating in interconnects.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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

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 head0.028
GPT teacher head0.330
Teacher spread0.302 · 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 designSimulation or modeling
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

Citations3
Published2010
Admission routes1
Has abstractyes

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