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Record W1977738893 · doi:10.1117/12.732664

Modeling of Si-based solar cells with V-grooved surface texture by Crosslight APSYS

2007· article· en· W1977738893 on OpenAlexaff
Yuanzhang Xiao, Michel Lestrade, Z. Q. Li, Z. M. S. Li

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsCrosslight Software (Canada)
Fundersnot available
KeywordsMaterials scienceCommon emitterOptoelectronicsTexture (cosmology)Solar cellSiliconAmorphous siliconAbsorption (acoustics)Polycrystalline siliconShort circuitOpticsRay tracing (physics)Layer (electronics)VoltageCrystalline siliconComputer scienceNanotechnologyElectrical engineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Based on Crosslight APSYS, two-dimensional simulations have been performed on Si-based solar cell devices especially those with V-grooved surface texture. These Si-based solar cells include rear-contacted cells and passivated emitter, rear totally diffused cells etc. The APSYS simulator is based on drift-diffusion theory with many advanced features. It can enable an efficient computation across the whole solar spectra by taking into account the effects of multiple layer optical interference and photon generation. The integrated ray-tracing module can compute optical absorption through the complex texture surface with multiple antireflection coating layers. Basic physical quantities like band diagram, optical absorption and generation can be demonstrated. The I-V characteristics with short-circuit current density and open-circuit voltage agree with the published experimental results and enhanced cell efficiency is shown with the V-grooved texture. The results are analyzed with respect to surface recombination, antireflection coating, bulk doping/resistivity and lifetime etc. Modeling capabilities for polycrystalline silicon and amorphous silicon cells are also discussed.

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 score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicThin-Film Transistor TechnologiesFrench-language works237,207