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Record W2326912472 · doi:10.1149/1.3096522

High-Speed Cu-TSV for 5:1 iTSV Applications

2009· article· en· W2326912472 on OpenAlexaff
Rozalia Beica, Paul Siblerud

Bibliographic record

VenueECS Transactions · 2009
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsInterconnectionMiniaturizationThrough-silicon viaProcess integrationMaterials scienceThree-dimensional integrated circuitProcess (computing)Layer (electronics)Computer scienceElectronic engineeringIntegrated circuitNanotechnologySiliconProcess engineeringEngineeringOptoelectronicsTelecommunications

Abstract

fetched live from OpenAlex

Transition from two-dimensional (2D) to three-dimensional (3D) integration seems to be critical for achieving miniaturization and increased performance and an unavoidable trend for the semiconductor industry. Because of the large variety of applications and requirements as well as a equipment and processes available today for 3D Interconnect, there are still questions about choosing the most suitable integration solutions that can not only provide the performance and advancements required for next generation electronic devices, however a cost effective implementation as well. This paper will address 3D integration using copper electrodeposited through silicon vias (TSV) technology. Various process parameters, such as via profile, seed layer uniformity, wettability and chemical stability as well as process parameters and equipment design were found to be critical in successfully filling TSV structures using copper electrodeposition. This paper will describe the effect of these factors with a focus on the challenges and costs associated with interconnect type TSV structures (iTSV).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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Same venueECS TransactionsSame topic3D IC and TSV technologiesFrench-language works237,207