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Record W2040401447 · doi:10.1109/tim.2014.2321461

Low-Contact Resistance Probe Card Using MEMS Technology

2014· article· en· W2040401447 on OpenAlexafffund
Nabeeh Kandalaft, Iftekhar Basith, Rashid Rashidzadeh

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2014
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsMicroelectromechanical systemsWaferContact resistanceWafer testingReturn lossMaterials scienceDie (integrated circuit)Integrated circuitElectrical engineeringInsertion lossElectronic circuitElectronic engineeringOptoelectronicsEngineeringNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

Multichannel die probing increases test speed and lowers the overall cost of testing. A new high-density wafer probe card based on MEMS technology is presented in this paper. MEMS-based microtest-channels have been designed to establish high-speed low-resistance connectivity between the die-under-test and the tester at the wafer level. The proposed test scheme can be used to probe fine pitch pads and interconnects of a new generation of 3-D integrated circuits. The proposed MEMS probe, which is fabricated with two masks, supports \(10^{6}\) lifetime touchdowns. Measurement results using a prototype indicate that the proposed architecture can be used to conduct manufacturing tests up to 38.6 GHz with less than -1-dB insertion loss while maintaining 11.4-m\(\Omega \) contact resistance. The measured return loss of the probe at 39.6 GHz is -12.05 dB.

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)0.001
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.024
GPT teacher head0.221
Teacher spread0.197 · 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

Citations15
Published2014
Admission routes2
Has abstractyes

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Same venueIEEE Transactions on Instrumentation and MeasurementSame topic3D IC and TSV technologiesFrench-language works237,207