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Record W2795632034 · doi:10.1109/tvlsi.2018.2817177

On the Analysis and the Mitigation of Power Supply Noise and Power Distribution Network Impedance Variation for Scan-Based Delay Testing Techniques

2018· article· en· W2795632034 on OpenAlexafffund
Claude Thibeault, Ghyslain Gagnon

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2018
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsElectronic engineeringNoise (video)Static timing analysisNoise marginVoltage droopPower (physics)Electrical impedanceIntermodulationComputer scienceEngineeringTransistorElectrical engineeringVoltageCMOSVoltage regulatorPhysics

Abstract

fetched live from OpenAlex

In this paper, we analyze the impact of the power supply noise and the power distribution network (PDN) impedance variation on the timing margin in both modes for ICs with multiple clock domains. We investigate the so-called intermodulation products (IMPs). We show that IMPs are mainly induced by the dependent nature of the transistors. We also provide experimental results showing that scan-based delay testing can be optimistic with respect to the mission mode for maximum achievable nominal frequency prediction, even at lower clock frequencies. We also show that IMPs can induce timing margin fluctuations that can be larger than that of the ones induced by the voltage droop in the test mode. Using an improved HSpice simulation model of a PDN validated by experimental results, we also quantify the timing margin variation due to power noise in the test mode as a function of the clock frequency, including the so-called clock stretching phenomenon. Finally, we propose a robust test signal scheme for multiple clock domain chips. The simulation results reveal that this scheme is less sensitive to PDN impedance variation than that of the most popular existing test schemes, and that it provides timing margins closer to those obtained in the mission mode.

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)0.001
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 score0.774
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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