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Record W4416368549 · doi:10.1109/tdmr.2025.3634716

On-Silicon Characterization of CDM-Like Stress in Long Interconnects Using vf-TLP in Nanometric ICs

2025· article· W4416368549 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.

Bibliographic record

VenueIEEE Transactions on Device and Materials Reliability · 2025
Typearticle
Language
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsNMOS logicElectrostatic dischargeRobustness (evolution)CMOSDissipationCapacitanceElectric power transmissionInterconnectionTransmission line

Abstract

fetched live from OpenAlex

This work presents a comprehensive silicon-based validation methodology for Charged Device Model Electrostatic Discharge (CDM-ESD) protection strategies in a 28 nm thin-oxide CMOS process. The approach evaluates multiple protection topologies such as duo/trio diodes, and grounded-gate nMOS (ggNMOS), using very-fast Transmission Line Pulse (vf-TLP) testing. Failure analysis (FA) via Scanning Electron Microscopy (SEM) is used to correlate electrical degradation with physical damage post-stress. In-depth analysis is performed from three key perspectives: the influence of averaging window selection on I–V curve fidelity, extraction and interpretation of decoupling capacitance and energy dissipation efficiency under CDM-like stress. Results highlight both the strengths and limitations of each protection method under fast transients, offering insight into optimal ESD design for long interconnect paths. Practical enhancements to the vf-TLP setup are also discussed. This study identifies layout-induced failure mechanisms too. The proposed framework enhances CDM robustness validation and informs future ESD strategies in scaled nodes.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.249
Teacher spread0.241 · 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