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Record W3011856882 · doi:10.1063/1.5142855

A unified model for vertical doped and polarized superjunction GaN devices

2020· article· en· W3011856882 on OpenAlexaff
Haimeng Huang, Junji Cheng, Bo Yi, Weijia Zhang, Wai Tung Ng

Bibliographic record

VenueApplied Physics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsJFETDopingPillarElectric fieldMaterials scienceOptoelectronicsImpact ionizationBreakdown voltageCondensed matter physicsField-effect transistorMOSFETVoltageTransistorPhysicsIonizationEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

A unified model is proposed to characterize the breakdown voltage (BV) and specific on-resistance (Ron,sp) for vertical doped superjunction (d-SJ) and polarized superjunction (p-SJ) GaN power devices. This study is based on the recently published compensated-pillar superjunction (cp-SJ) structure. A two-dimensional model for the electric field is analytically formulated using the Green's function method. Numerical calculations and TCAD simulations demonstrate that, for a given pillar depth, the p-SJ device has a lower BV than the d-SJ device with a wide pillar width. However, when the pillar width is less than 200 nm, both devices demonstrate a maximum BV that is close to the intrinsic structure. The Ron,sp unified model for the cp-SJ device, taking into account the junction field-effect transistor (JFET) effect in the drift region, also demonstrates that the p-SJ device has a superior Ron,sp over the d-SJ device. Considering the recently published impact ionization coefficients, the BV of the p-SJ device is analytically modeled as a function of the pillar depth. Finally, by applying the Lambert W-function, an exact closed-form relationship between Ron,sp and BV is presented.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.677

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 studies0.0000.000
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 head0.027
GPT teacher head0.213
Teacher spread0.186 · 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 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

Citations11
Published2020
Admission routes1
Has abstractyes

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