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Employing Scanning Spreading Resistance Microscopy (SSRM) for Improving TCAD Simulation Accuracy of Silicon Carbide

2017· article· en· W2612702660 on OpenAlexaff
Rudolf Elpelt, Bernd Zippelius, Stefan Doering, Uwe Winkler

Bibliographic record

VenueMaterials science forum · 2017
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsSilicon carbideMaterials scienceSpreading resistance profilingCharacterization (materials science)DopingSiliconMicroscopyProcess (computing)OptoelectronicsNanotechnologyElectronic engineeringComputer scienceOpticsComposite materialEngineering

Abstract

fetched live from OpenAlex

Computer-Aided-Design for the prediction of the technology process and the physical device properties (TCAD) is a key tool for the development and improvement of new device concepts as well as for the analysis and understanding of device properties and device behavior under application conditions. Apart from physical device models and parameters the precise process simulation of implanted doping profiles is mandatory for a sufficient prediction quality of the subsequent device simulations. In order to verify and improve the accuracy of process simulation, we employ the – for silicon carbide – relatively new method of Scanning Spreading Resistance Microscopy (SSRM) for the characterization of doping profiles.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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