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Record W2013421959 · doi:10.1117/12.657067

Laser-induced oxidation of metallic thin films as a method for creating grayscale photomasks

2006· article· en· W2013421959 on OpenAlexaff
Glenn H. Chapman, Yuqiang Tu, Chinheng Choo, Jun Wang, David K. Poon, Marian Chang

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPhotomaskMaterials scienceGrayscaleOpticsLaserLacquerThin filmPhotoresistOptoelectronicsNanotechnologyResistPixelCoating

Abstract

fetched live from OpenAlex

Bimetallic Bi/In films demonstrate grayscale levels after exposed with different laser powers due to controlled film oxidation. Although large optical density (OD) change from 3.0 OD to 0.22 OD at 365 nm was observed, these films show a rapid and nonlinear OD change with laser power, which is not desirable for fine control of grayscale levels. This paper aims to explore and evaluate some new metal films as possible candidates for direct-write grayscale photomask applications. Sn/In, Al/Zn, Bi/In/O and Al/In films were DC-sputtered onto glass slides and then were raster-scanned by argon CW laser. Among these films, the highest OD change at 365nm was found in Sn/In film, Al/Zn shows the most linear relation of OD to laser power modulation, and Bi/In/O has the best over-all performance as a potential grayscale mask material. A grayscale test photomask of 16×16, 20μm squares over the full OD range was made using Bi/In/O and a test exposure created squares of different heights on regular photoresist. Interference lithography using 266nm DUV has been utilized to investigate the resolution limit of these bimetallic films, which can generate much finer structures. The true resolution limit of Bi/In should be at least less than 50nm.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.245
Teacher spread0.231 · 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.

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

Citations10
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSemiconductor materials and devicesFrench-language works237,207