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Record W2751712013 · doi:10.1063/1.5001424

Via sidewall insulation for through cell via contacts

2017· article· en· W2751712013 on OpenAlexafffund
Mathieu de Lafontaine, Abdelatif Jaouad, Maxime Darnon, Maïté Volatier, Richard Arès, S. Fafard, Vincent Aimez

Bibliographic record

VenueAIP conference proceedings · 2017
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersUniversité de LyonCentre National de la Recherche ScientifiqueUniversité de Sherbrooke
KeywordsMaterials sciencePlasma-enhanced chemical vapor depositionOptoelectronicsFabricationAtomic layer depositionLayer (electronics)Chemical vapor depositionComposite material

Abstract

fetched live from OpenAlex

Over the past few years, through cell via contacts (TCVC) architecture has been the object of a growing interest to replace standard front side and backside contact on concentrated photovoltaic (CPV) cells. The technology is based on transferring the front side contact to the backside using insulated and metallized vias. This architecture could reduce shading and series resistance, thus increasing device efficiency. However, the processes involved in TCVC fabrication increases the risk of creating short-circuit, reducing significantly the efficiency of the solar cell. Therefore, the electrical insulation must be defect free. In this paper, an insulation validation protocol is proposed in order to thoroughly study the insulation quality. This process has been used to compare two insulation deposition techniques candidates: plasma-enhanced chemical vapor deposition (PECVD) and plasma-enhanced atomic layer deposition (PEALD). Results show that the insulation validation protocol presents several strengths such as revealing defects otherwise unobservable even with scanning electron microscopy. PECVD insulation presents several insulation defects whereas PEALD presents almost no defects making it suited for via insulation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.825

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 communication0.0000.002
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 head0.024
GPT teacher head0.239
Teacher spread0.215 · 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 designSimulation or modeling
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

Citations4
Published2017
Admission routes2
Has abstractyes

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