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Record W2127941601 · doi:10.1017/s1431927612013414

Three-Dimensional Electron Energy Deposition Modeling of Cathodoluminescence Emission near Threading Dislocations in GaN and Electron-Beam Lithography Exposure Parameters for a PMMA Resist

2012· article· en· W2127941601 on OpenAlexaff
Hendrix Demers, N Poirier-Demers, Matthew R. Phillips, Niels de Jonge, Dominique Drouin

Bibliographic record

VenueMicroscopy and Microanalysis · 2012
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCathodoluminescenceResistMaterials scienceElectron-beam lithographyElectronOptoelectronicsLithographyCathode rayDeposition (geology)Electron beam-induced depositionNanotechnologyTransmission electron microscopyPhysicsScanning transmission electron microscopy

Abstract

fetched live from OpenAlex

The Monte Carlo software CASINO has been expanded with new modules for the simulation of complex beam scanning patterns, for the simulation of cathodoluminescence (CL), and for the calculation of electron energy deposition in subregions of a three-dimensional (3D) volume. Two examples are presented of the application of these new capabilities of CASINO. First, the CL emission near threading dislocations in gallium nitride (GaN) was modeled. The CL emission simulation of threading dislocations in GaN demonstrated that a better signal-to-noise ratio was obtained with lower incident electron energy than with higher energy. Second, the capability to simulate the distribution of the deposited energy in 3D was used to determine exposure parameters for polymethylmethacrylate resist using electron-beam lithography (EBL). The energy deposition dose in the resist was compared for two different multibeam EBL schemes by changing the incident electron energy.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.247
Teacher spread0.235 · 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

Citations19
Published2012
Admission routes1
Has abstractyes

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