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Record W1784050215 · doi:10.1109/commad.1996.610133

Implantation and annealing of Cu in InP for electrical isolation: microstructural characterisation

2002· article· en· W1784050215 on OpenAlexaff
David J. Llewellyn, M. C. Ridgway, F. Gerald, Michael Davies, S. Rolfe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsNational Research Council CanadaInstitute for Microstructural Sciences
Fundersnot available
KeywordsAnnealing (glass)Materials scienceRutherford backscattering spectrometrySchottky diodeTransmission electron microscopyIon implantationOptoelectronicsGetterResistive touchscreenAnalytical Chemistry (journal)MetallurgyNanotechnologyIonThin filmChemistry

Abstract

fetched live from OpenAlex

The formation of metallic precipitates to produce embedded Schottky barriers within a conductive layer has been investigated as a potentially new form of implantation-induced isolation. Accordingly, Cu-implanted InP has been characterised with Rutherford backscattering spectrometry, transmission electron microscopy and secondary ion mass spectrometry as functions of implantation and annealing temperatures. Substrates implanted at room temperature were amorphised, resulting in greater post-anneal disorder in the form of microtwins and dislocations. However, annealing-induced Cu diffusion was reduced in such samples as attributed to gettering at end-of-range disorder. Additional defect centres, potentially Cu-based precipitates, were also observed. Further to the structural characterisation presented herein, complementary electrical measurements are necessary to deduce the appropriate combination of residual disorder and precipitate concentration to yield electrical compensation. This will ultimately determine the viability of this isolation technology for producing extremely resistive substrates for very high frequency devices.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.417

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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