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Record W2023943051 · doi:10.1002/mop.27776

A Novel Modeling of Millimeter‐Wave Al<sub>0.27</sub>Ga<sub>0.73</sub>N/AlN/GaN Hemt Based on Artificial Neural Network

2013· article· en· W2023943051 on OpenAlexaff
Zhiqun Cheng, Xi Wang, Qi‐qun Zhang

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsCarleton University
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsHigh-electron-mobility transistorExtremely high frequencyMicrowaveArtificial neural networkMaterials scienceOptoelectronicsTransistorMillimeterGallium nitrideElectrical engineeringElectronic engineeringComputer scienceEngineeringPhysicsNanotechnologyTelecommunicationsArtificial intelligenceOptics

Abstract

fetched live from OpenAlex

ABSTRACT An artificial neural network (ANN) was used for modeling millimeter‐wave Al 0.27 Ga 0.73 N/AlN/GaN high electron mobility transistor (HEMT) with multi‐biases in this article. Millimeter‐wave Al 0.27 Ga 0.73 N/AlN/GaN HEMT with gate width of 2 × 75 µm and gate length of 0.3 µm was designed and fabricated at first, and then its performance was measured for modeling. NeuroModelerPlus_V2.1E software was trained to learn the input‐output relationship from the data about DC and AC performances of proposed device. ANN DC model and AC model were set up and embedded in software of ADS together to form a model of millimeter‐wave Al 0.27 Ga 0.73 N/AlN/GaN HEMT, which can be used in circuit design. The results of the simulation of the model showed that it corresponded with the measurement results. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2124–2127, 2013

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.213
Teacher spread0.196 · 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 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

Citations6
Published2013
Admission routes1
Has abstractyes

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