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Record W1451158137 · doi:10.7567/jjap.54.096602

Effects of coating material on the fabrication accuracy of focused ion beam machining of insulators

2015· article· en· W1451158137 on OpenAlexaff
Hang-Eun Joe, Jae‐Hyeong Park, Seong Hyeon Kim, Gyuho Kim, Martin Byung‐Guk Jun, Byung-Kwon Min

Bibliographic record

VenueJapanese Journal of Applied Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFabricationMachiningCoatingMaterials scienceIon beamFocused ion beamBeam (structure)IonComposite materialMetallurgyOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

Focused ion beam (FIB) machining of insulators is a crucial process in the rapid prototyping of nanodevices for optical applications. A conductive material is generally coated on the insulator prior to FIB machining to achieve high fabrication accuracy. In this paper, we report on the effects on machining accuracy of four coating materials: Pt, Ni, Ag, and Co. The dimensional accuracy at channel sidewalls was improved by selecting a coating material that induces charge-carrier generation in a small range. The geometric and electrical characteristics of the FIB-machined surfaces were evaluated to elucidate the association between the fabrication accuracy and the range of charge-carrier distribution.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.308

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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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 teacher head, 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

Citations3
Published2015
Admission routes1
Has abstractyes

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