MétaCan
Menu
Back to cohort
Record W2045911878 · doi:10.5555/2492708.2493098

AIR (aerial image retargeting): a novel technique for in-fab automatic model-based retargeting-for-yield

2012· article· en· W2045911878 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

VenueDesign, Automation, and Test in Europe · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Photolithography Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRetargetingLithographyAerial imageProcess windowComputer scienceWaferOptical proximity correctionProcess (computing)Artificial intelligenceReliability (semiconductor)Matching (statistics)Computer visionElectronic engineeringImage (mathematics)Materials scienceEngineeringNanotechnologyOptoelectronicsMathematics

Abstract

fetched live from OpenAlex

In this paper, we present a novel methodology for identifying lithography hot-spots and automatically transforming them into the lithography-friendly design space. This fast model-based technique is applied at the mask tape-out stage by slightly shifting and resizing the designs. It implicitly does a similar functionality as that of the Process Window OPC (PWOPC) but more efficiently. Being a relatively fast technique it also offers the means of providing the designer with all the design systematic deviations from the actual (on-wafer) parameters by including it in the parameter-extraction flow. We applied this methodology successfully to 28-nm Metal levels and showed that it efficiently (better quality and faster) improves the lithography-related yield and reliability issues.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.756
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.254
Teacher spread0.234 · 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