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Record W3106433277 · doi:10.1049/iet-cds.2020.0026

Fine resolution delay tuning method to improve the linearity of an unbalanced time‐to‐digital converter on a Xilinx FPGA

2020· article· en· W3106433277 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIET Circuits Devices & Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsÉcole de Technologie SupérieurePolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsField-programmable gate arrayComputer scienceLinearityLeast significant bitRouting (electronic design automation)Differential nonlinearityComputer hardwareEmbedded systemElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

In this study, a method for fine adjustment of Xilinx field programmable gate array (FPGA) routing delays is proposed and applied to improve the linearity of an unbalanced multi‐measurement time‐to‐digital converter (TDC). The delay control method increases load capacitances of interconnect points of switch matrices by small amounts using additional connections to unused interconnects in the FPGA fabric. The novel delay control method uses the tool command language (TCL) scripting feature available in the Xilinx Vivado tool to automatically add wires into a fully placed and routed design. A total of 61 additional wires were successfully and automatically added to reduce the differential and integral non‐linearities of the target TDC from 0.51 and −0.54 LSB to 0.05 and 0.06 LSB, respectively (reduction factors of 10.2 and 9) for an LSB equal to 333 ps.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.690

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 head0.020
GPT teacher head0.258
Teacher spread0.238 · 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