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Record W4401247043 · doi:10.1109/jflex.2024.3437369

Simulation and Modeling of Emerging Heterojunction Transistors: Toward Rational Design and Optimization

2024· article· en· W4401247043 on OpenAlexafffund
Hocheon Yoo, Ryoma Hayakawa, Yutaka Wakayama, Chang‐Hyun Kim

Bibliographic record

VenueIEEE Journal on Flexible Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of Korea
KeywordsHeterojunctionTransistorRational designComputer scienceSystems engineeringMaterials scienceEngineeringNanotechnologyOptoelectronicsElectrical engineering

Abstract

fetched live from OpenAlex

This review summarizes the recent progress in the simulation and modeling of two important types of thin-film heterojunction transistors. Both structures functionally rely on the formation of a lateral p-n junction inside a single source-to-drain channel. However, different extents of vertical overlap between the p- and n-type semiconductor films produce unique switching characteristics of the respective systems. Experimental evidences behind recent theoretical models and the predictive capabilities of these models are illustrated with published examples. Simulation results of multivalued logic (MVL) circuits built with these heterojunction transistors are also discussed to emphasize practical aspects of device modeling for circuit- and system-level optimization.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.286
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations2
Published2024
Admission routes2
Has abstractyes

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