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Record W2127951937 · doi:10.1109/phycon.2003.1236842

Discrete-time control system design for a Reactive Ion Etching (RIE) system

2004· article· en· W2127951937 on OpenAlexaff
Nicolae Tudoroiu, Valery D. Yurkevich, K. Khorasani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)Multivariable calculusController (irrigation)Transient (computer programming)Process (computing)Reactive-ion etchingStability (learning theory)Computer scienceTracking (education)Discrete time and continuous timeControl systemControl engineeringEtching (microfabrication)EngineeringMaterials scienceControl (management)MathematicsNanotechnology

Abstract

fetched live from OpenAlex

The problem of designing a robust controller to solve a tracking control problem for improving plasma characteristics in Reactive Ion Etching process is studied. The presented design methodology is based on the construction of a two-time scale motions of the closed-loop system. It has been shown that under discussed conditions the proposed dynamical controller induces a two time-scale separation of the fast and slow modes in the closed-loop system. Stability conditions imposed on the fast and slow motions can ensure that the full-order closed-loop system achieves the desired properties so that the out-put transient performances are insensitive to parameter variations and external disturbances. Simulation results on a multivariable auto regressive moving average model of a RIE process are presented to demonstrate the utility of the proposed algorithm.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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 designSimulation or modeling
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
Published2004
Admission routes1
Has abstractyes

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