MétaCan
Menu
Back to cohort
Record W2051017609 · doi:10.1117/12.746953

Use of layout automation and design-based metrology for defect test mask design and verification

2007· article· en· W2051017609 on OpenAlexaff
Chris Spence, Cyrus Tabery, Andre Poock, Arndt C. Duerr, T. Witte, Jan Fiebig, Jan Heumann

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Photolithography Techniques
Canadian institutionsAdvanced Micro Devices (Canada)
FundersBundesministerium für Bildung und Forschung
KeywordsReticleMetrologyCritical dimensionWaferComputer scienceOptical proximity correctionSoftwareLithographyAutomationDimension (graph theory)Dimensional metrologyElectronic design automationEngineering drawingElectronic engineeringMaterials scienceEmbedded systemOpticsEngineeringMechanical engineeringNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

This paper studies the impact of shape and local environment (pattern layout) on the ability to detect defects on the reticle and the extent to which they affect the dimension of the printed image on the wafer. The authors have made extensive use of design information to perform a thorough evaluation. OPC software was used to generate mask data that was comparable to product mask data. Defects were placed on the post-OPC layout and OPC software was also used to simulate the dimension of the defective features as printed on the wafer. "Design Based Metrology" was used to create accurate metrology recipes to support wafer and mask metrology. Ultimately the procedures described in this paper allow a direct correlation to be made between reticle inspectability and the impact of the same defects on wafer CD. Data is presented for the case of the Contact Hole layer of a "65nm" Logic technology, though the methods described in the paper are applicable to all 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 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)0.001
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 score0.381
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvancements in Photolithography TechniquesFrench-language works237,207