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Record W2122690958 · doi:10.1504/ijmtm.2008.016778

Bulk micromachining for SOI based microsystems using double side XeF<SUB align=right>2 etching

2008· article· en· W2122690958 on OpenAlexaff
Avinash K. Bhaskar, Muthukumaran Packirisamy, Rama Bhat

Bibliographic record

VenueInternational Journal of Manufacturing Technology and Management · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsSilicon on insulatorMaterials scienceMicrosystemMicrofabricationMicroelectromechanical systemsWaferSurface micromachiningEtching (microfabrication)OptoelectronicsXenon difluorideFabricationBulk micromachiningPhotolithographyWafer bondingSiliconNanotechnologyLayer (electronics)Chemistry

Abstract

fetched live from OpenAlex

Microfabrication is pivotal in any microsystem synthesis. Even through silicon is the most common material used in the Micro Electro Mechanical System (MEMS) foundry, Silicon on Insulator (SOI) wafers are very promising for the fabrication of Micro Opto Electro Mechanical System (MOEMS) Devices such as micromirrors due to their better optical reflectivity, low residual stress and compatibility with silicon microfabrication processes. The present work explores the possibility of patterning and using controlled pulse etching method involving Xenon difluoride (XeF2) to etch SOI wafers to realise micromirrors. Pulse-etching method is proposed due to its better control and etching uniformity. This paper also presents the process flow for the fabrication of SOI based micromirrors through double side XeF2 micromachining which involves mask preparation, photolithography and pulse etching. The presented results also include etch characteristics obtained on SOI wafers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.246
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2008
Admission routes1
Has abstractyes

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