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Record W4396965586 · doi:10.1049/pbpo241f_ch1

Semiconductor power devices

2024· book-chapter· en· W4396965586 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInstitution of Engineering and Technology eBooks · 2024
Typebook-chapter
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsSemiconductorSemiconductor devicePower (physics)Electrical engineeringOptoelectronicsMaterials sciencePhysicsEngineeringNanotechnology

Abstract

fetched live from OpenAlex

Power semiconductor devices are the fundamental components of power conversion products ubiquitous in energy conversion applications. Advances in power electronics devices would impact all these applications and open avenues for more applications that were previously not possible. This chapter presented an overview of conventional silicon devices and their real-world applications. Due to the ever-increasing demand for higher efficiency and power density, wide bandgap materials like SiC and GaN-based devices have gained significant adoption in recent years. Various SiC and GaN devices and their basic properties have been provided. The chapter included several examples of real semiconductor characterization waveforms to illustrate the practical behavior of the semiconductors for power converter designers. Also, several considerations on measuring switching losses and paralleling semiconductor devices have been provided. There are ongoing efforts to develop even more efficient power devices using ultra-wide bandgap materials like gallium oxide, aluminum nitride, and diamond that can enable even higher efficiencies and power densities in future power conversion systems.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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