MétaCan
Menu
Back to cohort
Record W2749376897 · doi:10.1063/1.5001312

The performance and limitations of FPGA-based digital servos for atomic, molecular, and optical physics experiments

2018· article· en· W2749376897 on OpenAlexafffund

Bibliographic record

VenueReview of Scientific Instruments · 2018
Typearticle
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsUniversity of British Columbia
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsServomechanismServoCompensation (psychology)Benchmark (surveying)Digital filterServo controlTransfer functionServo driveDigital controlFilter (signal processing)

Abstract

fetched live from OpenAlex

In this work, we address the advantages, limitations, and technical subtleties of employing field programmable gate array (FPGA)-based digital servos for high-bandwidth feedback control of lasers in atomic, molecular, and optical physics experiments. Specifically, we provide the results of benchmark performance tests in experimental setups including noise, bandwidth, and dynamic range for two digital servos built with low and mid-range priced FPGA development platforms. The digital servo results are compared to results obtained from a commercially available state-of-the-art analog servo using the same plant for control (intensity stabilization). The digital servos have feedback bandwidths of 2.5 MHz, limited by the total signal latency, and we demonstrate improvements beyond the transfer function offered by the analog servo including a three-pole filter and a two-pole filter with phase compensation to suppress resonances. We also discuss limitations of our FPGA-servo implementation and general considerations when designing and using digital servos.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.240
Teacher spread0.218 · 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

Citations25
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueReview of Scientific InstrumentsSame topicGeophysics and Sensor TechnologyFrench-language works237,207