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Record W4378187422 · doi:10.14447/jnmes.v26i1.a01

Reliability Assessment of Hybrid Silicon-Silicon Carbide IGBT Implemented on an Inverter for Photo Voltaic Applications

2023· article· en· W4378187422 on OpenAlexvenueno aff
Sainadh Singh Kshatri, Nagineni Venkata Sireesha, DSNM Rao, Ranjith Kumar Gatla, Thallapalli Kranti Kumar, P. Chandra Babu, S. Saravanan, Neerudi Bhoopal, Devineni Gireesh Kumar

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Silicon carbideInsulated-gate bipolar transistorSiliconInverterMaterials scienceReliability engineeringOptoelectronicsElectronic engineeringElectrical engineeringEngineeringMetallurgyVoltagePower (physics)Physics

Abstract

fetched live from OpenAlex

Recent Advancements in the semiconductor technology leads to the use of Silicon Carbide (SiC) materials in the design of power switches due to its wide band gap that performs superior compared to conventional Silicon material.Nevertheless, the cost of manufacturing IGBT with the SiC material is of major concern.Hence, this article proposes a hybrid Si-SiC based IGBT to improve the performance and reliability.The hybrid Si-SiC IGBT consist of Si-IGBT with SiC Feedback Diode.A test case of 600V/30A hybrid Switch (Si-IGBT (IGW30N60H3)/SiC-diode (C3D20060D)) is considered and implemented on a 3 kW PV inverter.Mission Profile oriented reliability analysis is carried out using PLECS thermal model at two different atmospheric conditions and its effectiveness is evaluated in comparison with conventional Si IGBT.Monte Carlo simulation is implemented to calculate the B 10 lifetime.The population size of 10000 with 5 % variation is considered.The improved B 10 lifetime and reliability with the proposed hybrid Si-SiC IGBT is obtained at both India and Denmark locations.

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)0.001
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.304
Teacher spread0.281 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of New Materials for Electrochemical SystemsSame topicSilicon Carbide Semiconductor TechnologiesFrench-language works237,207