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Record W4387303214 · doi:10.1117/12.2677780

Fabrication and characterization of zone plates for EUV applications

2023· article· en· W4387303214 on OpenAlexafffund
Remko van den Hurk, Robert Peters, Mirwais Aktary

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Photolithography Techniques
Canadian institutionsApplied Nanotools (Canada)
FundersUniversity of Alberta
KeywordsExtreme ultraviolet lithographyMaterials scienceFabricationZone plateEtching (microfabrication)OpticsElectron-beam lithographyMolybdenumCharacterization (materials science)LithographyDiffractionDiffraction efficiencyOptoelectronicsScanning electron microscopeDry etchingResistNanotechnologyComposite materialMetallurgyPhysics

Abstract

fetched live from OpenAlex

There is demand for optics for EUV applications including microscopy and diagnostic devices for EUV lithography exposure masks. Au and W are common materials for such applications, but they have relatively low theoretical diffraction efficiency. Ru is a good candidate for 13.5 nm wavelength applications as it has greater theoretical efficiency than Au or W. It also has the benefit of oxidizing less during plasma etching or on exposure to air. Oxidation can be a concern for materials with higher theoretical zone plate efficiency such as molybdenum because it can lead to a significant decrease in theoretical efficiency. Additionally, molybdenum also presents more etching challenges than Ru, particularly concerning sidewall verticality. Our work involves the fabrication and characterization of diffraction gratings and zone plates for 13.5 nm EUV applications. The fabrication of W and Ru phase zone plates with 80 nm outer zone width through electron beam lithography and plasma etching is presented. Characterization of the zone plate through SEM and STEM imaging, as well as EDX analysis, was performed. A comparison between the W and Ru zone plates is given.

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: none
Teacher disagreement score0.906
Threshold uncertainty score0.157

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)0.0000.000

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 head0.010
GPT teacher head0.253
Teacher spread0.242 · 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
Published2023
Admission routes2
Has abstractyes

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