MétaCan
Menu
Back to cohort
Record W3190058841 · doi:10.1088/1361-6641/ac1963

A dual-gate and Γ-type field plate GaN base E-HEMT with high breakdown voltage on simulation investigation

2021· article· en· W3190058841 on OpenAlexaff
Jialin Li, Yian Yin, Ni Zeng, Fengbo Liao, Mengxiao Lian, Xichen Zhang, Keming Zhang, Yong Zhang, Jingbo Li

Bibliographic record

VenueSemiconductor Science and Technology · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsHigh-electron-mobility transistorOptoelectronicsBreakdown voltageMaterials scienceDual (grammatical number)VoltageElectrical engineeringTransistorEngineering

Abstract

fetched live from OpenAlex

Abstract This paper proposes to insert a buried P-type gate (BPT gate) under the channel layer of the recessed MIS-high electron mobility transistor (HEMT) to form a dual-gate HEMT. And through simulation calculation, the device performance is calculated and the working mechanism of the BPT gate is explained in detail. Compared with a conventional AlGaN/AlN/GaN HEMT and a gate-recessed MIS-HEMT, the dual-gate HEMT has a higher breakdown voltage (1126 V) and a larger threshold voltage (1.5 V). The results show that inserting a BPT gate in a gate-recessed MIS-HEMT can increase the threshold voltage and improve the breakdown characteristics. Through the optimization of the device structure, it is found that the combination of the MIS-gate and the Γ-type field plate based on the dual-gate device can obtain a higher breakdown voltage. Its breakdown voltage can reach 3041 V, and the figure of merit is as high as 3.56 GW cm −2 . This reveals that the combination of dual-gate and Γ-type field plate has great potential in the manufacture of enhancement-mode and high-voltage power transistors.

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)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.240
Teacher spread0.228 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSemiconductor Science and TechnologySame topicGaN-based semiconductor devices and materialsFrench-language works237,207