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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 OpenAlex
Avinash K. Bhaskar, Muthukumaran Packirisamy, Rama Bhat

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.116
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.246
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