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Record W2324455340 · doi:10.1149/06411.0199ecst

A New Method to Increase the Doping Efficiency of Proton Implantation in a High-Dose Regime

2014· article· en· W2324455340 on OpenAlexaff
Moriz Jelinek, Johannes G. Laven, R. Job, Werner Schustereder, Hans‐Joachim Schulze, Mathias Rommel, L. Frey

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsPassivationAnnealing (glass)Materials scienceProtonHydrogenDopingSiliconOptoelectronicsAnalytical Chemistry (journal)NanotechnologyComposite materialChemistryNuclear physicsEnvironmental chemistry

Abstract

fetched live from OpenAlex

Besides the application of local lifetime control, proton implantations can be used to create deep donor profiles in crystalline silicon. At a certain annealing temperature, the maximum hydrogen-related donor (HD) concentration is limited to a few 10 15 cm -3 in the end-of-range depth of the radiation damage profile. This behavior is explained with a passivation of the shallow donors due to an excess supply of hydrogen at high proton doses. The impact of hydrogen remaining in the substrate from prior processing steps is investigated. As a countermeasure to the over-decoration effect, a pre-annealing step at elevated temperatures is investigated. This procedure is fully applicable in a commercial manufacturing environment. Experimental results from spreading resistance measurements are shown that clearly show the beneficial effect of the pre-conditioning. DLTS results of pre-conditioned samples are reported, that show a dominant deep defect level that was previously assigned to be vacancy related.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.643
Threshold uncertainty score0.225

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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
Published2014
Admission routes1
Has abstractyes

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