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Record W2122149637 · doi:10.1109/acc.2002.1023960

In-situ process control for semiconductor manufacturing

2002· article· en· W2122149637 on OpenAlexaff
James Taylor, Thomas K. Whidden, Zhao Xiaozhong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsDalhousie UniversityUniversity of New Brunswick
Fundersnot available
KeywordsSemiconductor device fabricationChemical processProcess controlChemical vapor depositionFabricationProcess (computing)SemiconductorNucleationFourier transform infrared spectroscopyProcess engineeringChemical reactionComputer scienceNanotechnologyMaterials scienceEngineeringOptoelectronicsChemistryChemical engineering

Abstract

fetched live from OpenAlex

There is a critical need for exact, real-time reaction control of the chemical vapor deposition (CVD) systems that are used for semiconductor device manufacture. The chemical kinetic relationships underlying the fabrication processes, while they have been modeled in certain cases have, in most instances, not been experimentally confirmed. These models are especially needed to effectively control particle nucleation within CVD reactors. The lack of chemical data on these systems is, at least in part, due to the fact that reliable, suitably configured sensors have not been generally available. Fourier-transform infrared (FT-IR) spectrometry is being used to provide the required sensing, and the chemical kinetic relationships involved in device manufacture are being understood and modeled. A prototype control system has been developed using an FTIR sensor to control the reaction chemistry for a specific CVD process, and a plan for extending and commercializing this technology has been created. These recent accomplishments are described in this paper.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.227
Teacher spread0.209 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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