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Record W4386072912 · doi:10.11159/htff23.118

Numerical modelling of Inductor Optimization for Silicon Crystal Growth with Pedestal Method

2023· article· en· W4386072912 on OpenAlexvenueno aff
Kirils Surovovs, Anatoly Kravtsov, J. Virbulis

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPedestalInductorSiliconMaterials scienceOptoelectronicsElectronic engineeringComputer scienceMechanical engineeringElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

The pedestal method [1] is a method of crystal growth, where the feed material is melted from above by high-frequency inductor, and the grown crystal is pulled from a molten zone that is located on top of the feed material (pedestal).An advantage of this method is the absence of contact between the molten material and other system parts.Another advantage is its relative simplicity in comparison with another crucible-free method -the well-known floating zone method, where the feed material is located above the inductor, and thus it is harder to control the melting front.For silicon crystal growth, the pedestal method can be cost-effective in comparison with the floating zone method, if large diameter polycrystalline rods are available [2].In the present work, the pedestal method is modelled numerically [3].High-frequency electromagnetic field from the main inductor and middle-frequency field from the additional side inductor are simulated.Then the shape of the phase boundaries is calculated by solving heat transport equation and moving the melting and crystallization interfaces according to heat balance.The numerical model is based on the previous model, first introduced for floating zone method [4].To get the most desirable shape of phase boundaries (e.g., high distance between the melting and crystallization interfaces), highfrequency inductor optimization is performed with the algorithm of gradient descent.The most recent results include the consideration of meniscus angle of the free melt surface in the algorithm's target function.In other words, process stability is dependent not only on preventing the collision between the grown crystal and the pedestal, but also on preventing the melt spilling over the pedestal rim.Another novelty of the study is the inclusion of the side inductor power into the set of input parameters for the gradient descent.In the results of the study, several shapes of high-frequency inductor are obtained and compared for different diameters of crystal and pedestal.The obtained results are helping to increase the diameter of silicon crystals that can be grown from pedestal in the experiments.It could make the pedestal method more efficient, because the larger crystal diameter is, the more electronical schemes can be simultaneously produced from one crystal wafer.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.663

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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