MétaCan
Menu
Back to cohort
Record W3203700235 · doi:10.1109/aces53325.2021.00162

Recent Advance in Neuro- Transfer Function-Assisted Yield-Driven EM Optimization

2021· article· en· W3203700235 on OpenAlexaff
Jianan Zhang, Feng Feng, Qi‐Jun Zhang

Bibliographic record

Venue2021 International Applied Computational Electromagnetics Society Symposium (ACES) · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCarleton University
Fundersnot available
KeywordsYield (engineering)Computer scienceProcess (computing)Component (thermodynamics)Transfer functionEngineeringMaterials sciencePhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Yield-driven electromagnetic (EM) optimization is a key component in microwave design due to fabrication tolerances and manufacturing uncertainties. Neuro-transfer function (neuro-TF) methods hold great potential to accelerate the overall yield optimization process by replacing the EM-based fine models with well-trained neuro- TF surrogates. This paper provides an overview of recent advance in neuro- TF -assisted yield-driven EM optimization, with a focus on the recently reported adaptively weighted yield-driven EM optimization incorporating neuro-TF surrogate. A four-pole waveguide filter example is used to demonstrate the advantages of this advanced approach.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venue2021 International Applied Computational Electromagnetics Society Symposium (ACES)Same topicMicrowave Engineering and WaveguidesFrench-language works237,207