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Record W2011179407 · doi:10.1063/1.4887117

Depth profile reconstructions of electronic transport properties in H+ MeV-energy ion-implanted n-Si wafers using photocarrier radiometry

2014· article· en· W2011179407 on OpenAlexaff
Rui Tai, Chinhua Wang, Jingpei Hu, Andreas Mandelis

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWaferIon implantationMaterials scienceThermal diffusivitySemiconductorOptoelectronicsIonChemistry

Abstract

fetched live from OpenAlex

A depth profiling technique using photocarrier radiometry (PCR) is demonstrated and used for the reconstruction of continuously varying electronic transport properties (carrier lifetime and electronic diffusivity) in the interim region between the ion residence layer and the bulk crystalline layer in H+ implanted semiconductor wafers with high implantation energies (∼MeV). This defect-rich region, which is normally assumed to be part of the homogeneous “substrate” in all existing two- and three-layer models, was sliced into many virtual thin layers along the depth direction so that the continuously and monotonically variable electronic properties across its thickness can be considered uniform within each virtual layer. The depth profile reconstruction of both carrier life time and diffusivity in H+ implanted wafers with several implantation doses (3 × 1014, 3 × 1015, and 3 × 1016 cm−2) and different implantation energies (from 0.75 to 2.0 MeV) is presented. This all-optical PCR method provides a fast non-destructive way of characterizing sub-surface process-induced electronic defect profiles in devices under fabrication at any intermediate stage before final metallization and possibly lead to process correction and optimization well before electrical testing and defect diagnosis becomes possible.

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 score0.116
Threshold uncertainty score0.490

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 head0.012
GPT teacher head0.194
Teacher spread0.182 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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