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Record W2055752109 · doi:10.1109/iscas.2013.6572034

A simple analog CMOS design tool using transistor dimension-independent parameters

2013· article· en· W2055752109 on OpenAlexaff
Omar Abdelfattah, I. Shih, Gordon W. Roberts

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsMcGill University
Fundersnot available
KeywordsCMOSTransistorTransistor modelSpiceComputer scienceElectronic engineeringAbstractionDimension (graph theory)Simple (philosophy)Circuit designSet (abstract data type)Node (physics)BiasingMixed-signal integrated circuitIntegrated circuitElectrical engineeringVoltageEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper presents an approach to fully characterize a transistor in any CMOS technology using a set of dimension-independent parameters. The proposed approach creates a layer of abstraction that circumvents the SPICE transistor model complexity and replaces it with a set of normalized parameters. The proposed approach combined with the designer's knowledge of the circuit operation facilitates solving for near optimal, if not optimal, circuit behavior. The required small and large-signal behavior is achieved, while the dc biasing at each node in the circuit is defined at the same time. A simple design tool, called ACDNP, based on the proposed concept was developed. The results predicted by ACDNP and the Spectre circuit simulator are in excellent agreement.

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 categoriesnone
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 score0.810
Threshold uncertainty score0.959

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 head0.029
GPT teacher head0.211
Teacher spread0.182 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

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