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Record W1532410448

Wafer post-processing for a reconfigurable wafer-scale circuit board

2009· article· en· W1532410448 on OpenAlexaff
Moufid Radji, Ahmed Lakhssassi, Mohammed Bougataya, Anas A. Hamoui, Ricardo Izquierdo

Bibliographic record

VenueEspace ÉTS (ETS) · 2009
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec en OutaouaisMcGill University
Fundersnot available
KeywordsWaferCMOSWafer-scale integrationInterconnectionReliability (semiconductor)Electronic engineeringIntegrated circuitMicrofabricationComputer scienceElectronic circuitEtching (microfabrication)Embedded systemEngineeringElectrical engineeringMaterials scienceNanotechnologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

The WaferBoard™ rapid prototyping platform for electronic systems is proposed as a tool to help meet today's tight delivery time, performance and reliability constraints. At the core of WaferBoard™ is the WaferIC™, a wafer-scale reconfigurable CMOS circuit. At the surface of this complex circuit is a sea of identical contacts, any pair of which can be interconnected through a mesh grid network called WaferNet™. The user can simply deposit packaged integrated circuits on the smart active surface, and then a complex interconnect pattern between these ICs can be established in a matter of minutes. As is the case with development of any novel technology, design of the platform poses several technical challenges. Postprocessing tasks to be accomplished on the CMOS wafer are laid out hereafter. A .18µm CMOS TestChip fabricated to validate the WaferIC™ concept on a 1/100th scale is outlined. Furthermore, sample microfabrication results, such as TSV etching, are presented along with thermo-mechanical investigation outcomes.

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)0.000
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.222
Teacher spread0.209 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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