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Comparison of Different Novel Chip Separation Methods for 4H-SiC

2015· article· en· W2247748489 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

VenueMaterials science forum · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsWafer dicingMaterials scienceWaferDiamondBreakageLaserChipLaser ablationOptoelectronicsComposite materialOpticsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Mechanical blade dicing is a state-of-the-art technique for the chip separation of SiC devices. Due to the hardness of SiC this technique suffers from low feed rate and high wear of the diamond coated dicing blade, resulting in the risk of uncontrolled tool breakage during the dicing process. With the upcoming transition to 150 mm diameter of SiC wafers this technique will most probably reach its limit. For dicing SiC wafers of those diameters on a productive scale three alternative dicing technologies are considered in this paper: ablation laser dicing, Stealth Dicing and Thermal Laser Separation. All these methods are based on laser processing. The benefits of these technologies are discussed in detail and compared to the classical mechanical diamond blade dicing, including a brief summary of first experimental results on each of the three laser dicing technologies.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.254
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.086
GPT teacher head0.437
Teacher spread0.352 · 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