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Record W2080655355 · doi:10.1109/mcas.2002.1045855

Microelectromechanical systems and system-on-chip connectivity

2002· article· en· W2080655355 on OpenAlexafffund
Sazzadur Chowdhury, Majid Ahmadi, William C. Miller

Bibliographic record

VenueIEEE Circuits and Systems Magazine · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Windsor
FundersCMC Microsystems
KeywordsMicroelectromechanical systemsModular designInterconnectionChipEngineeringDie (integrated circuit)Electronic engineeringCMOSEmbedded systemComputer scienceElectrical engineeringComputer hardwareMechanical engineeringTelecommunicationsMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

The interconnection of microelectromechanical systems (MEMS) and other devices to a system-on-chip (SoC) implementation is described. MEMS technology can be used to fabricate both application specific devices and the associated micropackaging system that will allow for the integration of devices or circuits, made with non-compatible technologies, with a SoC environment. In the primary example presented, MEMS technology has been used to develop an acoustical array sensor for a hearing instrument application and also to provide a custom micropackaging solution suitable for in-the-ear canal implantation. A MEMS based modular micropackaging solution consisting of MEMS socket submodules and an insertable/removable microbus card has been developed to provide the necessary packaging and connectivity requirements. The modular socket concept can also be used for many other purposes, such as temporarily connecting a CMOS die to a SoC implementation of a die tester using MEMS based cantilevered bridge-type microspring contacts to provide connectivity to the die under test.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.004

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 head0.022
GPT teacher head0.208
Teacher spread0.187 · 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

Citations6
Published2002
Admission routes2
Has abstractyes

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Same venueIEEE Circuits and Systems MagazineSame topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207