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Record W1585838201 · doi:10.1109/essder.2004.1356502

Back-etched super-junction LDMOST on SOI

2004· article· en· W1585838201 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSilicon on insulatorDiodeOptoelectronicsBreakdown voltageEtching (microfabrication)Materials scienceSubstrate (aquarium)Siliconp–n junctionVoltageElectrical engineeringSemiconductorNanotechnologyEngineering

Abstract

fetched live from OpenAlex

Conventional super junction LDMOSTs (SJLDMOSTs), fabricated on an SOI substrate, suffer from low breakdown voltage due to substrate-depletion effects. In this work, a back etched SJLDMOST (BSJLDMOST) on SOI is proposed to overcome this problem by eliminating the silicon substrate under the device. The electrical characteristics of the BSJLDMOST on a 0.8 /spl mu/m SOI film were investigated. The device with 15.5 /spl mu/m of SJ region exhibits a breakdown voltage of 317 V, a specific on-resistance of 48.3 m/spl Omega/cm/sup 2/ and a charge on-resistance figure of merit of 4.1 /spl Omega/nC. To verify the back etching concept and the suppression of the substrate depletion effect, super-junction diodes (BSJDs) were implemented. These diodes feature a threefold improvement in breakdown voltage over conventional super junction diodes (SJDs) implemented without removing the silicon substrate on the back of the device. A discussion of how the BSJLDMOST can be optimized to break the silicon limit is also provided.

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 categoriesInsufficient payload (model declined to judge)
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 score0.046
Threshold uncertainty score0.999

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)0.0010.002

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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