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Record W2088187105 · doi:10.1063/1.3110722

<i>In situ</i> x-ray diffraction study of metal induced crystallization of amorphous germanium

2009· article· en· W2088187105 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Physics · 2009
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsPolytechnique Montréal
FundersFonds Wetenschappelijk OnderzoekVlaamse regeringU.S. Department of Energy
KeywordsCrystallizationMaterials scienceAmorphous solidGermaniumAmorphous metalSiliconAmorphous siliconMetalCrystallographyAnalytical Chemistry (journal)Chemical engineeringMetallurgyChemistryCrystalline siliconOrganic chemistry

Abstract

fetched live from OpenAlex

Metal induced crystallization (MIC) is a technique that lowers the crystallization temperature of amorphous semiconductors. The process has mainly been used to influence the crystallization of amorphous silicon (a-Si) and multiple studies on this subject have already been performed. The research of the MIC of amorphous Ge (a-Ge) has been mostly limited to the use of a Ni or Al film. This paper focuses on the characterization of the crystallization behavior of a-Ge films in the presence of 20 transition metals (Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W, Mn, Re, Fe, Ru, Co, Rh, Ir, Ni, Pd, Pt, Cu, Ag, Au, and Al). The kinetics of the crystallization process are also systematically studied for the seven metals that lower the initial crystallization temperature the most. In addition, the influence of the thickness of the metal film was determined for the case of a Au and Al film. A comparison of the influence of the various metals on a-Ge and a-Si is made and the similarities and differences are discussed using existing models for the MIC process.

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.

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 score0.175
Threshold uncertainty score0.492

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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