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Record W4248110443 · doi:10.1149/ma2014-02/31/1654

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

2014· article· en· W4248110443 on OpenAlexaff
Moriz Jelinek, Johannes G. Laven, R. Job, Werner Schustereder, Hans‐Joachim Schulze, Mathias Rommel, L. Frey

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsHydrogenDopingMaterials scienceProtonAnnealing (glass)Radiation damageIrradiationRadiationGetterSiliconRange (aeronautics)HeliumAtomic physicsAnalytical Chemistry (journal)OptoelectronicsChemistryOpticsNuclear physicsPhysicsMetallurgyComposite material

Abstract

fetched live from OpenAlex

Proton-implantation doping offers a feasible method to modify the doping profile in semiconductor grade silicon in depths ranging up to several hundred micrometers at comparatively low thermal budgets. The hydrogen-related donors are radiation-induced defect complexes decorated by hydrogen. During the proton implantation, typically in the energy range of several 100 keV to several MeV and with fluences in the range between about 10 13 cm -2 and 10 15 cm -2 , intrinsic radiation defects are induced. During a successive anneal in the temperature range between about 300—500 °C the implanted hydrogen diffuses from its projected range through the radiation damage profile and decorates the radiation-induced defects or defect complexes, thus activating, the hydrogen-related donors. Due to this basic mechanism, the profile shape of the hydrogen-related donor profiles is closely correlated to the initial damage profile of the radiation-induced lattice defects. The profiles, hence, exhibit an extended penetrated range with an approximately constant concentration followed by an expressed peak near the end-of-range of the protons. The achievable doping concentration of the hydrogen-related donors induced by the proton implantation and successive annealing is, however, typically limited to a few 10 15 cm -3 . Earlier studies, based mainly on co-implantations of helium and hydrogen, have led us to propose this to be due to an over-decoration effect of the hydrogen-related donors by excess hydrogen. Based on this assumption, we present a pre-conditioning method based on vacancy-related gettering sites in order to reduce the concentration of excess hydrogen. The procedure comprises a pre-implant with protons and an annealing step at elevated temperatures above the range typically used for proton-implantation doping and is thus fully applicable to a commercial manufacturing environment. During this pre-conditioning, thermally stable higher-order defect complexes are created that are for the most part electrically inactive. These defect complexes act as gettering centers and may in effect reduce the concentration of free hydrogen after a second implant. Experimental results based on spreading resistance measurements are presented in this report that clearly show the beneficial effect of the pre-conditioning and its ability to help overcome several limitations of proton-implantation doping of high-purity silicon. The figure illustrates the effect of the new pre-conditioning method on two magnetic Czochralski-grown silicon wafers. The dashed line represents the carrier concentration profile after a 3-MeV implantation with a proton dose of 4×10 14 cm -2 and subsequent annealing at 490 °C for 5 h. The solid line depicts a profile resulting from the same main process steps where the wafer has been pre-conditioned before. It appears that the peak concentration could be increased by a factor of 3 to approximately 2×10 15 cm -3 . For consecutive studies it may also be of interest that the pre-conditioning results in a steadily increasing carrier concentration up to a depth of 80 µm from the surface.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.257
Teacher spread0.246 · 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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