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Record W4411541241 · doi:10.12737/2219-0767-2025-62-70

Architecture of a test structure for verification of libraries of standard elements in silicon based on a pipeline-distribution approach

2025· article· en· W4411541241 on OpenAlexaff
S. V. Gavrilov

Bibliographic record

VenueModeling of systems and processes · 2025
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsPipeline (software)Computer scienceStandard cellMultiplexerCMOSComputer engineeringComputer architectureElectronic engineeringIntegrated circuitEngineeringProgramming languageMultiplexingOperating system

Abstract

fetched live from OpenAlex

The paper proposes an architecture of a test structure for verifying libraries of standard cells in silicon, based on a pipeline-distributive approach. This approach allows reducing both the number of inputs and outputs and the area occupied by the test structure on a crystal in comparison with traditional approaches. The components of the test structure and the relationships between them are listed. The main attention is paid to the use of multiplexers and demultiplexers for controlling the processes of selection, control and output of signals. Several options for arrang-ing auxiliary hierarchical blocks and the corresponding blocks of automated formation of input actions for combinational cells are considered. A numerical assessment of the characteristics of each of the considered options is performed. A comparative analysis of the application of the proposed test structure for verifying libraries of standard digital elements in silicon is carried out. The proposed architecture was successfully applied in a test crystal for verification of three libraries of standard elements developed using the basic technology of JSC MERI CMOS 90 nm. The completeness of verification of the proposed test structure compared to the use of circuits from the ISCAS’85/89 sets is 6.19-6.31 times greater and reaches 99.94%. The area of the proposed structure is 2.29-4.65 times smaller.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score0.306

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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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