MétaCan
Menu
Back to cohort
Record W2223362452 · doi:10.1149/06906.0091ecst

Electrografted P4VP as Dielectric in High Aspect Ratio TSV: Surface Preparation and Thermomechanical Consideration

2015· article· en· W2223362452 on OpenAlexafffund
T Dequivre, Elias Al Alam, Julien Plathier, Andreas Ruëdiger, Gessie Brisard, Serge A. Charlebois

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaInstitut national de la recherche scientifiqueUniversité de Sherbrooke
KeywordsMaterials scienceThrough-silicon viaDielectricSiliconSubstrate (aquarium)Conformal mapDeposition (geology)Composite materialLayer (electronics)MicrosystemAqueous solutionOptoelectronicsNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

A challenge to most 3D integration approaches is the deposition of the Through-Silicon-Via (TSV) dielectric layer. A TSV is traditionally electrically insulated from the silicon substrate by a thin SiO 2 film. As TSV aspect ratio gets higher, conformal SiO 2 dielectric deposition becomes much more challenging. Alchimer has proposed as dielectric, the use of a highly conformal organic (poly-4-vinylpyridine, P4VP) as dielectric, electrografted through electrochemical reduction of diazonium salts in aqueous media. Surface cleaning prior to electrografting process is a challenge, especially in via last approach. Moreover, the thermomechanical behavior of P4VP coated TSVs remains unknown. This information is most relevant for the layout optimization of 3D integrated microsystems. We will discuss the TSV surface preparation challenge prior to the electrografting process and present the first measurements of the stress induced in the silicon substrate around TSV insulate by P4VP.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.012
GPT teacher head0.229
Teacher spread0.216 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

Explore more

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