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Record W4401250820 · doi:10.1109/jstqe.2024.3437193

High-Speed Electro-Optic Plasmonic Modulator for CMOS Non-Contact Wafer-Level Testing

2024· article· en· W4401250820 on OpenAlexafffund
Maryam Sadat Amiri Naeini, Pierre Berini

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCMOSMaterials scienceOptoelectronicsElectro-optic modulatorWaferPlasmonOpticsOptical modulatorPhase modulationPhysics

Abstract

fetched live from OpenAlex

Wafer-level testing is an important step for process and quality control of electronic chips in integrated circuit (IC) manufacturing which occurs before packaging. The process of wafer probing in its conventional contacting schemes, becomes more complicated as ICs move to smaller technology nodes and more compact designs, greatly increasing testing costs. Non-contact optical wafer probing can overcome physical probing complications, reducing costs, and increasing throughput and reliability. In this article, a CMOS compatible, broadband (22 GHz), small footprint (5 μm dia.) plasmonic electro-optic modulator of low insertion loss (4 dB) and wide optical working bandwidth (100 nm) is proposed and demonstrated as a potential solution for wafer-level optical testing. The device modulates in reflection an incident optical carrier emerging from an optical fiber in a non-contact arrangement, to work as a data output channel from the wafer. A modulation depth of over 2% is achieved which should be sufficient to meet the requirements of wafer-level testing. The device can be placed anywhere on wafer.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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.

Study designSimulation or modeling
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
Published2024
Admission routes2
Has abstractyes

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