MétaCan
Menu
Back to cohort
Record W4281249671 · doi:10.1139/cjp-2020-0535

Feasibility of two-dimensional die-level plasma process monitoring using spatially resolvable optical emission spectrometers

2022· article· en· W4281249671 on OpenAlexvenueno aff
Jin Young Lee, Dae‐Woong Kim, Min Sup Hur, Young‐Hoon Song, Hun-Jung Yi, Chang-Sug Lee, Nu-Ri Kim, Woo Seok Kang

Bibliographic record

VenueCanadian Journal of Physics · 2022
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsnot available
FundersKorea Institute of Energy Technology Evaluation and PlanningSamsungKorea Institute of Machinery and Materials
KeywordsPhysicsPlasmaIntensity (physics)Resolution (logic)Plasma diagnosticsOpticsSpectrometerPerpendicularLine (geometry)Atomic physicsGeometryNuclear physics

Abstract

fetched live from OpenAlex

We demonstrated the feasibility of die-level process monitoring using spatially resolvable optical emission spectrometers (SROESs) in a low-pressure plasma reactor. The spatially resolved Ar emission line intensities and their intensity ratios were obtained from uniformly generated and intentionally perturbed plasma by inserting conductors of known sizes at known locations. The sheath and perturbed plasma distributions were identified from the Ar emission line intensity and their intensity ratio in the lateral direction to the SROES installment orientation. However, the resolution in the axial direction was insufficient to identify the sheath and perturbed the plasma distribution. We propose the superimposition of measured plasma emission intensities from two perpendicularly aligned SROESs for die-level process monitoring. The insufficient axial resolution can be compensated for by the lateral resolution of another SROES, and die-level process monitoring using SROES can thus be realized.

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 score0.699
Threshold uncertainty score0.680

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 integrity0.0000.001
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 head0.049
GPT teacher head0.273
Teacher spread0.224 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicLaser-induced spectroscopy and plasmaFrench-language works237,207