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Record W1989154068 · doi:10.1016/j.egypro.2014.08.121

Industrial Cleaning Sequences for Al2O3-passivated PERC Solar Cells

2014· article· en· W1989154068 on OpenAlexfundno aff
Christopher Kranz, S. Wyczanowski, Ulrike Baumann, Silke Dorn, S. Queißer, Jürgen Schweckendiek, D. Pysch, Thorsten Dullweber

Bibliographic record

VenueEnergy Procedia · 2014
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsnot available
FundersInstitute of Gender and HealthSoutheastern Louisiana University
KeywordsPassivationWaferCommon emitterEtching (microfabrication)Materials scienceSiliconPorous siliconWet cleaningDiffusionPorositySolar cellOptoelectronicsLayer (electronics)Analytical Chemistry (journal)ChemistryNanotechnologyComposite materialChromatography

Abstract

fetched live from OpenAlex

In this paper, we investigate different industrial applicable cleaning sequences on test wafers and PERC solar cells in comparison to a laboratory type RCA clean. The cleaning sequences pSC1, HF/HCl, HF/O3 and HF/O3 show lifetimes between 1 ms and 2 ms which is comparable to a laboratory type RCA clean corresponding to a surface recombination velocity Spass below 15 cm/s. The pSC1, HF/HCl clean achieves lifetimes around 1 ms, whereas the PSG-etch shows poor cleaning quality with lifetimes around 500 μs. Reference PERC cells using a rear protection layer before texturing and diffusion demonstrate efficiencies up to 20.4% for the cleaning sequence pSC1, HF/HCl prior to passivation which is comparable to the RCA clean. The HF/O3 cleans result in lower PERC efficiencies up to 20.0% mainly due to a lower Fill Factor which is likely caused by etching of the emitter and hence increased contact resistance. Investigations of polished test wafers show that the cleaning sequences pSC1, HF/HCl, HF-Dip and pSC1, HF/HCl, HF/O3 are able to sufficiently remove porous silicon from the front side and simultaneously allowing excellent rear surface passivation. A first batch of PERC solar cell results with polished rear surface post texturing and POCl3 diffusion achieves efficiencies of up to 20.7% when applying an RCA clean. However, the pSC1, HF/HCl and pSC1 HF/O3 still exhibit significantly lower efficiencies since in this batch the porous silicon of the emitter was not yet sufficiently removed, which is subject to further optimization.

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 score0.286
Threshold uncertainty score0.638

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 head0.016
GPT teacher head0.188
Teacher spread0.172 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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