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Record W2168348385 · doi:10.1007/s00542-014-2198-4

Low-cost silicon wafer dicing using a craft cutter

2014· article· en· W2168348385 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicrosystem Technologies · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsWafer dicingWaferDie preparationWafer backgrindingMaterials scienceWafer testingSiliconSlicingEngineering drawingDie (integrated circuit)OptoelectronicsMechanical engineeringEngineeringNanotechnology

Abstract

fetched live from OpenAlex

This paper reports a low-cost silicon wafer dicing technique using a commercial craft cutter. The 4-inch silicon wafers were scribed using a crafter cutter with a mounted diamond blade. The pre-programmed automated process can reach a minimum die feature of 3 mm by 3 mm. We performed this scribing process on the top polished surface of a silicon wafer; we also created a scribing method for the back-unpolished surface in order to protect the structures on the wafer during scribing. Compared with other wafer dicing methods, our proposed dicing technique is extremely low cost (lower than $1,000), and suitable for silicon wafer dicing in microelectromechanical or microfluidic fields, which usually have a relatively large die dimension. The proposed dicing technique is also usable for dicing multiple project wafers, a process where dies of different dimensions are diced on the same wafer.

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 categoriesMeta-epidemiology (narrow)
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 score0.246
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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