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Record W2800205943 · doi:10.1116/1.5023590

Modeling of silicon etching using Bosch process: Effects of oxygen addition on the plasma and surface properties

2018· article· en· W2800205943 on OpenAlexfundno aff
Guillaume Le Dain, A. Rhallabi, Christophe Cardinaud, Aurélie Girard, Marie-Claude Fernandez, Mohamed Boufnichel, Fabrice Roqueta

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
FundersSTMicroelectronicsCanadian Bee Research Fund
KeywordsEtching (microfabrication)Deposition (geology)SiliconReactive-ion etchingPlasmaAnalytical Chemistry (journal)Materials sciencePlasma etchingOxygenChemistryNanotechnologyMetallurgyEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The authors developed a tool using a multiscale approach to simulate the silicon etching using Bosch process. Their study is focused on the analysis of the effect of the oxygen addition to C4F8 plasma during the deposition pulse. This is the complementary study that the authors have recently published which was dedicated to the Bosch process under pure SF6 plasma used in etching pulse and pure C4F8 plasma used in polymer deposition pulse. Parametric study about the effect of the oxygen percentage on the reactive species flux evolution and their impact on the deposition kinetic during the deposition pulse has been performed. The simulation results reveal that for a low %O2 in a C4F8/O2 plasma mixture, the atomic fluorine density increases because of the volume reactions, especially recombinations between CFx and O which favor the production of fluorine. This leads to the decrease of CFx to F flux ratio. Ion energy distribution functions (IEDF) plotting reveals the impact of both %O2 and mass of the positive ions on the IEDF shape. Finally, both the experimental and simulation results show that in their pressure range, the addition of O2 to C4F8 plasma has a weak impact on the silicon etch profile and the etching rate, except for a high %O2 which the etch anisotropy begins to be degraded.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.229
Teacher spread0.215 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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