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Record W2014177245 · doi:10.1155/2008/291686

MEMS Switches and SiGe Logic for Multi‐GHz Loopback Testing

2008· article· en· W2014177245 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueVLSI design · 2008
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsIBM (Canada)
FundersIBM CanadaGeorgia Institute of Technology
KeywordsPCI ExpressComputer scienceDebuggingJitterWaveformElectronic engineeringModular designComputer hardwareEmbedded systemElectrical engineeringEngineeringVoltageField-programmable gate array

Abstract

fetched live from OpenAlex

We describe the use of microelectromechanical system (MEMS) switches and SiGe logic devices for both passive and active loopback testing of wide data buses at rates up to 6.4 Gbps per signal. Target applications include HyperTransport, fully buffered DIMM, and PCI Express, among others. Recently introduced MEMS devices provide >7 GHz bandwidth in a very small package (needed to handle wide buses). SiGe logic supports >7 Gbps signals when active shaping of the waveform is required. Each loopback module typically supports between 9 and 16 differential channels. Multiple cards are used to handle applications with very wide buses or multiple ports. Passive cards utilize MEMS for switching between the loopback (self‐test) mode and traditional automated test equipment (ATE) source/receiver channels. Future active card designs may provide additional waveform‐shaping functions, such as buffering, amplitude attenuation/modulation, deskew, delay adjustment, jitter injection, and so forth. The modular approach permits precalibration of the loopback electronics and easy reconfiguration between debug or characterization testing and high‐volume production screening.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.471

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)0.0000.000

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 head0.204
GPT teacher head0.264
Teacher spread0.059 · 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