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Record W2178207328 · doi:10.1116/1.4936112

Deep germanium etching using time multiplexed plasma etching

2015· article· en· W2178207328 on OpenAlexafffund
Maxime Darnon, Mathieu de Lafontaine, Maïté Volatier, S. Fafard, Richard Arès, Abdelatif Jaouad, Vincent Aimez

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2015
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaInstitut National des Sciences Appliquées de LyonCentre National de la Recherche Scientifique
KeywordsGermaniumEtching (microfabrication)PassivationMaterials scienceReactive-ion etchingAspect ratio (aeronautics)PhotoresistOptoelectronicsDry etchingDeposition (geology)Plasma etchingSubstrate (aquarium)DiffusionSiliconAnalytical Chemistry (journal)NanotechnologyChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

There is a growing need for patterning germanium for photonic and photovoltaics applications. In this paper, the authors use a time multiplexed plasma etch process (Bosch process) to deep etch a germanium substrate. They show that germanium etching presents a strong aspect ratio dependent etching and that patterns present scallops mostly on the upper part (aspect ratio below 0.8). Passivation layers are formed during the passivation step by neutrals' deposition and are reinforced during the etching step by the redeposition of sputtered fluorocarbon species from the etch front. When the sidewalls are passivated, reactive neutrals diffuse through Knudsen-like diffusion down to the bottom of the pattern to etch the germanium. The Knudsen-like diffusion is responsible for the aspect ratio dependent etching and makes difficult the etching of holes with aspect ratios above 10 while trenches with aspect ratio of 17 are still etched faster than 2 μm/min.

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)0.003
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.227
Teacher spread0.206 · 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.

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

Citations16
Published2015
Admission routes2
Has abstractyes

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