MétaCan
Menu
Back to cohort
Record W2910660926 · doi:10.1139/tcsme-2017-1022

DESIGN OPTIMIZATION OF VERTICAL NEEDLE GEOMETRY FOR BUMP WAFER-LEVEL PROBING

2017· article· en· W2910660926 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsnot available
Fundersnot available
KeywordsRectangleTaguchi methodsWaferOffset (computer science)Orthogonal arrayMaterials scienceGeometryStress (linguistics)OpticsStructural engineeringAcousticsMathematicsComposite materialEngineeringPhysicsNanotechnologyComputer science

Abstract

fetched live from OpenAlex

The purpose of this paper is mainly to develop a method to use the Taguchi method with the L 18 (2 1 × 3 7 ) orthogonal array to obtain an optimal geometrical design of the vertical probing needle and base on various criteria to minimize the stress on the probing needle during wafer-level probing test. Furthermore, importance of the factors on the probing mark area ratio was also ranked. The results shows that as probe length, offset, and lower die gap increase, stress on the probing decrease. On the contrary, vertical probe bends decrease, stress on the probe increase. Furthermore, the body of vertical probe with rectangle cross-section is better than square and circular sharp.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

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 head0.046
GPT teacher head0.230
Teacher spread0.184 · 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