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Record W2115970566 · doi:10.1116/1.2167976

Electrical isolation of electrodes with submicron separation in a digital optical switch

2006· article· en· W2115970566 on OpenAlexaff
Sandy Ng, S. Abdalla, B. Syrett, Pedro Barrios, W. R. McKinnon, A. Delâge, Ilya Golub, S. Janz, J. Lapointe

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2006
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsInstitute for Microstructural SciencesCarleton University
Fundersnot available
KeywordsElectrodeTrenchMaterials scienceShadow maskOptoelectronicsEtching (microfabrication)Current (fluid)Shallow trench isolationIon implantationShadow (psychology)OpticsLayer (electronics)IonElectrical engineeringChemistryNanotechnology

Abstract

fetched live from OpenAlex

The electrodes in a carrier-injection-based digital optical switch are separated by a gap of less than 1μm. Good switch performance requires minimal current spreading and effective electrical isolation of these two electrodes without perturbing the underlying guided optical fields. Two methods of such isolation are modeled and experimentally compared. One is the etching of an e-beam-defined 0.5-μm-wide trench down to an etch stop layer, and the other is the implanting of oxygen ions using the electrodes as a shadow mask. Electrical simulation results suggest that injected current magnitudes and carrier concentration profiles are similar with both the trench and the ion implant, and so they should be equally effective in preventing current flow between the two branches. However, experimental results show that ion implantation produces better switching contrasts of greater than 20dB. We attribute this to the fact that implantation using the electrodes as a shadow mask results in an injected carrier distribution that more accurately reproduces the shape of the overlying electrode.

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 score0.033
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.003
GPT teacher head0.211
Teacher spread0.208 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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