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Analog Test Bus Structure for Wide-Bandwidth High-Frequency Measurements

2025· article· en· W4411726616 on OpenAlexaff
William D. Ledingham, Gordon W. Roberts

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsBandwidth (computing)Computer scienceElectronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Analog test buses facilitate the testing of analog components in integrated circuits through dedicated test ports and switches. IEEE 1149.4 is an analog test bus standard that can be calibrated with simple gain correction but cannot calibrate for other sources of error present at higher frequencies, leading to inaccurate measurements. This paper presents a novel analog test bus structure with greater calibration capabilities inspired by the vector network analyzer (VNA) calibration approach that uses multiple known calibration references. These references were integrated within the test bus constructed of voltage and current buffers. IEEE 1149.4 and the proposed test bus were first compared via simulation and then through a discrete component PCB implementation, both aimed at measuring a transimpedance amplifier (TIA). The simulation showed that the proposed test bus exactly measured the amplifier with little error while 1149.4 deviated at higher frequencies. These results were echoed in the physical experiment, with the proposed 4-port deviating slightly from the ideal due to the mismatch inherent in the PCB experiment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.013
GPT teacher head0.241
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

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