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Record W2891748079 · doi:10.1109/ipfa.2018.8452180

Analysis Methods and Strategies of Analog and Mix Signal Circuits in Power IC

2018· article· en· W2891748079 on OpenAlexaff
Gan Chye Siong Kenny, Hubert Beermann, Stephan Merzsch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsComputer scienceFault detection and isolationIntegrated circuitElectronic engineeringElectronic circuitSIGNAL (programming language)Fault (geology)TransistorIsolation (microbiology)Power (physics)Failure mode and effects analysisMixed-signal integrated circuitElectrical engineeringEngineeringReliability engineeringVoltage

Abstract

fetched live from OpenAlex

This paper describes the strategy and methods deployed to overcome complexities at various analysis steps systematically in analyzing analog or mix-signal circuits within power ICs. Methods and strategies include: 1.) Building up of universal application board as plug and play setup to verify the failure mode hence reducing setup time. 2.) Global plasma etching with end point detector to expose metal stacks which was implemented instead of FIB pad preparation prior to internal node measurement. 3.) Device characterization of suspicious transistors was measured on IC circuit while the IC is running in application mode. This method does not require physical circuit isolation. 4.) CAD simulation utilized as a tool for fault injection to confirm possible failure location. 5.) FIB as a local de-passivation technique to expose failing site or to perform further necessary fault isolation without altering the electrical failure.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.275
Teacher spread0.264 · 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 designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

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