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Record W1826950605 · doi:10.1109/iciprm.1993.380694

Residual impurities in high purity InP grown by chemical beam epitaxy

2002· article· en· W1826950605 on OpenAlexaff
T. Sudersena Rao, C. Lacelle, S. Rolfe, Louise Allard, S. Charbonneau, A. P. Roth, T. Steiner, M. L. W. Thewalt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsSimon Fraser UniversityInstitute for Microstructural Sciences
Fundersnot available
KeywordsImpurityAnalytical Chemistry (journal)Molecular beam epitaxyChemical beam epitaxyEpitaxyAcceptorResidualMaterials scienceChemistryMineralogyNanotechnologyCondensed matter physicsChromatography

Abstract

fetched live from OpenAlex

In the past few years, chemical beam epitaxy (CBE) has succeeded in producing high purity InP with residual carrier concentrations in the low 10/sup 14/ cm/sup -3/ range and liquid nitrogen temperature mobilities much higher than 10/sup 5/ cm/sup 2//Vs. The authors present the results of a study where they have combined electrical, chemical, and optical measurements to identify the residual impurities in InP layers grown with different growth parameters. It is shown that S and Si are the two major residual donor impurities in InP layers grown by CBE and that they originate from the gas sources. Arsenic contamination of InP layers is a common problem in gas source systems, particularly when a single cracker cell is used for both As and P sources. However this contamination can be greatly reduced with a thorough baking prior to InP growth. The concentration of acceptors is negligible and too low to allow the identification of the residual acceptor impurities. Under optimized growth conditions, InP layers with residual carrier concentrations less than 10/sup 14/ cm/sup -3/ can be routinely grown with 77 K mobilities larger than 2 /spl times/ 10/sup 5/ cm/sup 2//Vs.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 categoriesInsufficient payload (model declined to judge)
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 score0.609
Threshold uncertainty score0.994

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)0.0070.000

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 head0.211
Teacher spread0.201 · 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.

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
Published2002
Admission routes1
Has abstractyes

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