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Record W4322768359 · doi:10.1002/admi.202202489

Graphite Pellicle: Physical Shield for Next‐Generation EUV Lithography Technology

2023· article· en· W4322768359 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Materials Interfaces · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Ottawa
FundersSungkyunkwan University
KeywordsExtreme ultraviolet lithographyPhotomaskMaterials scienceReticleNanotechnologyLithographyFabricationGraphiteElectronicsCharacterization (materials science)ResistOptoelectronicsComposite materialElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Extreme ultraviolet lithography (EUVL) is widely employed in the electronics, automotive, military, and AI computing areas for IC chip fabrication. A pellicle is a thin and transparent membrane that protects a costly photomask, known as a reticle, during the EUVL process. The fabricated IC chip can be disastrous without a pellicle. When a particle lands on a photomask, it frequently results in a faulty pattern, which leads to chip failure and lower production yield. A nanometer‐thick graphite (NGF) has demonstrated tremendous potential for addressing optical, mechanical, thermal, and chemical criteria among potential pellicle materials such as carbon allotropes, Si, SiNx, and Si‐Mo‐Nb. This review summarizes current progress in NGF pellicles, including large‐scale material fabrication (up to 135 mm × 135 mm), transfer method for freestanding form, and practical characterization methods. Current significant challenges and future opportunities for NGF pellicles are also discussed in order to facilitate a critical transition from lab‐scale research to industrial‐scale implementation.

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.

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 score0.019
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.343
Teacher spread0.291 · 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