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Record W2144275718 · doi:10.5555/1266366.1266420

A symbolic methodology for the verification of analog and mixed signal designs

2007· article· en· W2144275718 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
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsConcordia University
Fundersnot available
KeywordsCorrectnessSymbolic computationSymbolic trajectory evaluationComputer scienceRepresentation (politics)Set (abstract data type)ComputationMixed-signal integrated circuitSymbolic data analysisSIGNAL (programming language)Theoretical computer scienceAlgebra over a fieldAlgorithmProgramming languageMathematicsIntegrated circuitModel checking

Abstract

fetched live from OpenAlex

We propose a new symbolic verification methodology for proving the properties of analog and mixed signal (AMS) designs. Starting with an AMS description and a set of prop-erties and using symbolic computation, we extract a normal mathematical representation for the system in terms of re-currence equations. These normalized equations are used along with an induction verification strategy defined inside the computer algebra system Mathematica to prove the cor-rectness of the properties. We apply our methodology on a third order ∆Σ modulator. 1

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.123

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.179
GPT teacher head0.337
Teacher spread0.158 · 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

Citations33
Published2007
Admission routes1
Has abstractyes

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