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Record W4288601508

Ge/Si Virtual Substrate for III-V multi-junction solar cells

2019· preprint· en· W4288601508 on OpenAlexaff
Youcef A. Bioud, Abderraouf Boucherif, Dominique Drouin, Richard Arès

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2019
Typepreprint
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsSubstrate (aquarium)Materials scienceSiliconOptoelectronicsComputer scienceGeology
DOInot available

Abstract

fetched live from OpenAlex

Multi-junction III-V concentrator cells have the most important energy conversion efficiency, competing with any other solar cell technology. Various cell designs have been proposed to achieve the efficiency of over 40% [1], [2]. Germanium appears to be the material of choice for the bottom cell to cover the IR part of solar spectrum. A high efficiency of 40.7% was measured for a metamorphic three-junction GaInP/GaInAs/Ge cell at 240 suns [3]. This architecture has achieved system efficiency of 30% [4]. A road map based on the integration of Ge on Si wafers can be a solution to replace the bulk Ge substrates for significant cost savings. However, due to the important 4.2% lattice mismatch between Si and Ge, strain is introduced in epitaxial Ge layers on Si which leads to the generation of a misfit dislocations network. Dislocations act as recombination centers for carriers and strongly influence the solar cell performance [5]. Several techniques of dislocation reduction were reported in the recent years [6]. However, the treading dislocations density (TDD) in the Ge layer remains higher than 106 cm-1. In this work, we propose a new architecture of low-cost virtual substrate engineering with acceptable TDD and surface roughness for III-V multi-junction solar cell. Ge/Si substrate was treated with post epitaxial process to create a denser nanovoids region in Ge layer according to the mass transport mechanism [7]. Fig 1. shows cross section SEM view of self-assembled nanovoids in Ge layer/Si obtained after electrochemical porosification and thermal annealing [8]-[17]. The idea here is to use voids as a barrier for threading dislocations propagation. In this presentation, we will present detailed optimization of the porosification and the annealing of Ge on Si and study the effect of different process parameters on TDD and on surface roughness. The resulting surface can be used as the starting point for the deposition of the III–V junctions in the 3J PV cell. This engineered substrate concept is promising for the fabrication of dislocation-free Ge on Si substrate and would result in a significant cost reduction in multijunction solar cells cost.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.206
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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