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Record W2078423352 · doi:10.1143/jjap.45.2992

AC Power Loss and Signal Coupling in Very Large Scale Integration Backend Interconnects

2006· article· en· W2078423352 on OpenAlexaff
C. C. Chen, Hsuan‐Ling Kao, Chi‐Hung Liao, Albert Chin, S. P. McAlister, C. C.

Bibliographic record

VenueJapanese Journal of Applied Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCopper Interconnects and Reliability
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCoupling (piping)Materials scienceCoupling lossSIGNAL (programming language)Power (physics)DielectricDielectric lossOptoelectronicsMetalElectrical engineeringPhysicsComposite materialOpticsComputer scienceEngineering

Abstract

fetched live from OpenAlex

Both the coupling and AC power losses in integrated circuit interconnects in the radio-frequency regime have been measured. The AC power loss decreases with decreasing length, decreasing spacing, and increasing inter metal dielectric (IMD) thickness of parallel metal lines. The unwanted signal coupling and cross-talk monotonically decrease with increasing spacing and decreasing length of the parallel metal lines. However, increasing the IMD thickness from 0.7 to 6 µm improves the low frequency performance but not the maximum operation frequency. Using a high-resistivity Si (HRS) substrate the AC power loss is significantly reduced but is traded off with an increase in coupling loss. The most effective method of reducing both the AC power and coupling losses is the combined use of three dimensional (3D) integration and an HRS, which gives larger than 1–2 orders of magnitude improvement, up to 20 GHz.

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)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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