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Record W2772463964 · doi:10.1109/tvlsi.2017.2773481

Analog Layout Retargeting With Process-Variation-Aware Hybrid OPC

2017· article· en· W2772463964 on OpenAlexafffund
Xuan Dong, Lihong Zhang

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Photolithography Techniques
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandResearch and Development Corporation of Newfoundland and LabradorCanada Foundation for Innovation
KeywordsRetargetingProcess variationComputer scienceProcess (computing)Variation (astronomy)Integrated circuit layoutElectronic engineeringComputer architectureIntegrated circuitArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

For high-quality analog layout retargeting, in this brief we propose a hybrid optical proximity correction (OPC) methodology, which features special handling against geometry manufacturing deviation caused by process variation (PV). Based on the unique nature of analog layouts, accuracy limitation of the rule-based OPC (RB-OPC) is compensated by geometry preprocessing operations in our proposed layout retargeting flow, and is further shrunk by a local model-based OPC postprocessing operation without incurring any chip area increment. An innovative RB PV-band correction scheme is developed to preserve analog circuit performance against possible PV. The experiments show that our analog layout retargeting flow integrated with the proposed PV-aware hybrid OPC can achieve much higher efficiency with even lower mask complexity and edge placement error compared to alternative methods.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations3
Published2017
Admission routes2
Has abstractyes

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