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Record W2042394787 · doi:10.1117/12.809975

Optical characterization of the mask writing process in bimetallic grayscale photomasks

2009· article· en· W2042394787 on OpenAlexaff
James M. Dykes, Glenn H. Chapman

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPhotomaskMaterials scienceGrayscaleOpticsLaserLaser power scalingOptoelectronicsRaster graphicsThin filmRaster scanComputer sciencePixelResistNanotechnologyLayer (electronics)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

Bimetallic thin films of Bi/In and Sn/In oxidize becoming transparent under laser exposure. By controlling the laser power, direct-write binary and grayscale photomasks can be produced with the mask's transparency, or optical density (OD), ranging between ~3.0 (unexposed) to <0.22 OD (fully exposed). An OD measurement system has been developed that provides real time OD and laser exposure power measurements while the masks are being written. Measurements are obtained for each combination of films, characterizing their response when patterned with a raster-scanned v-groove mask. The characterization is performed by writing v-groove step patterns and modifying the mask's writing parameters such as velocity, line spacing and step width. Stationary results demonstrate Sn/In takes longer to expose compared to Bi/In. With a moving beam, the oxidation of Sn/In also occurs over a wider power range suggesting film materials with delayed or slower oxidations may offer power ranges that are better suited for grayscale masks. A narrow power range is less desirable for grayscale as more control is required over the writing laser. The stationary exposures also demonstrate both films can produce >64 distinct OD levels provided there is sufficient control over the laser power and exposure duration. The physical characteristics of the films are also examined to determine a more accurate method of verifying each film's composition. Combining weight, area, and thickness measurements allows for better characterization of the films as the thickness for bi-layer films are found to differ significantly from the sum of the individual layers.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.220
Teacher spread0.210 · 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

Citations2
Published2009
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