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Record W2060400339 · doi:10.1109/inec.2013.6465969

Facile nanometer thick native oxide based passivation of silicon for high efficiency photovoltaics

2013· article· en· W2060400339 on OpenAlexaff
Nazir P. Kherani, Zahidur Chowdhury

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPassivationWaferMaterials sciencePhotovoltaicsSiliconPlasma-enhanced chemical vapor depositionOptoelectronicsCarrier lifetimeCrystalline siliconAmorphous siliconNanotechnologyPhotovoltaic systemElectrical engineeringLayer (electronics)Engineering

Abstract

fetched live from OpenAlex

Fabrication of low cost solar cells using ultra-thin (approximately 20 μm) silicon wafers is a viable route given the significant potential of reduced material cost and its versatility to a range of portable and terrestrial applications. Low temperature processing is a compelling opportunity for the synthesis of high-efficiency ultra-thin silicon wafers. Further, excellent surface passivation attainable through facile low temperature processing techniques is an essential enabler for effective manufacturing of ultra-thin silicon solar cells, and thus paving the way for high-efficiency low-cost silicon foil photovoltaics. This article presents a novel low temperature passivation scheme using approximately 1 nm thick facile native oxide and 75 nm PECVD SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> . A maximum lifetime of 1.7 ms has been obtained for the passivation scheme. Moreover, the passivated wafers were also used to fabricate Back Amorphous-Crystalline Silicon Heterojunction (BACH) cells using double side polished n-type FZ wafers. A maximum cell efficiency of 16.7% is obtained for facile native oxide -PECVD SiN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> bilayer passivated cells having V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> of 641 mV, J <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</sub> of 33.7 mA/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and fill-factor of 0.77 for a 1 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> untextured cell (all measurements having been performed under AM 1.5 global spectrum illumination).

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 score0.110
Threshold uncertainty score0.369

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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