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Record W4281297002 · doi:10.3390/rs14102486

The Development of Frequency Multipliers for Terahertz Remote Sensing System

2022· article· en· W4281297002 on OpenAlexaff
Yong Zhang, Chengkai Wu, Xiaoyu Liu, Li Wang, Chunyue Dai, Jianhang Cui, Yukun Li, Nicholas Kinar

Bibliographic record

VenueRemote Sensing · 2022
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Saskatchewan
FundersNational Natural Science Foundation of China
KeywordsTerahertz radiationSchottky diodeComputer scienceFrequency multiplierContext (archaeology)PlanarDiodeElectronic engineeringDivision (mathematics)OpticsOptoelectronicsMaterials sciencePhysicsEngineeringTelecommunicationsBandwidth (computing)Mathematics

Abstract

fetched live from OpenAlex

This paper summarizes the development of novel Schottky-diode-based terahertz frequency multipliers. The basic structure and manufacturing process of planar Schottky barrier diodes (SBDs) are reviewed, along with other diode structures that have been proposed in the literature. A numerical modeling method for the novel diodes in the context of terahertz frequency multipliers is presented, which includes 3D electromagnetic (EM) modeling, electro-thermal modeling and modeling of physical non-ideal effects. Furthermore, a general design methodology for developing terahertz frequency multipliers is introduced, involving a sub-division design method (SDM), a global design method (GDM) and a half-sub-division and half-global design method (HS-HGDM). These methods are summarized and compared for 110 GHz and 220 GHz frequency multipliers in the context of communication and imaging applications. Laboratory measurements of these multipliers show good agreement with numerical simulations. Finally, several classic terahertz remote sensing systems are reviewed, and a 220 GHz remote sensing system established using novel frequency multipliers for security inspection purposes is presented along with associated imaging results.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.919
Threshold uncertainty score0.681

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 studies0.0010.000
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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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 designOther design
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

Citations15
Published2022
Admission routes1
Has abstractyes

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