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Fast Electromigration Simulation for Chip Power Grids

2023· article· en· W4377969795 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

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCopper Interconnects and Reliability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectromigrationSpeedupChipComputer scienceGridVoid (composites)VoltagePower gridElectronic engineeringPower (physics)Parallel computingElectrical engineeringMaterials scienceEngineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Electromigration (EM) continues to be a serious concern for large chip design. We are focused on EM in the on-chip power grid, because grid lines carry mostly unidirectional currents and because of the very large sizes of modern grids. In the last few years, the capability to simulate EM has become available by simulating the stress in metal lines, which is the main cause of EM-induced failures. In this work, we have improved on the state of the art by developing a new EM simulator that is both faster and has better features than previous work. The work builds on recent results on the equivalence between stress and voltage, and introduces both a model reduction technique that provides up to 4.2X speedup, and a very efficient method for updating the grid currents during the void growth phase.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.897

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 studies0.0000.000
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 head0.019
GPT teacher head0.298
Teacher spread0.279 · 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

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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