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Record W3112926552 · doi:10.1109/ted.2020.3038623

Low-Temperature Self-Aligned-Silicide-Capable Transistor Process Using Solid-Phase-Epitaxy and Lift-Off for Hybrid Substrates

2020· article· en· W3112926552 on OpenAlexaff
Roohollah Samadzadeh Tarighat, Siva Sivoththaman

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2020
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceOptoelectronicsPlasma-enhanced chemical vapor depositionSiliconTransistorAmorphous solidEpitaxyElectron mobilityFabricationBilayerNanotechnologyVoltageElectrical engineeringLayer (electronics)ChemistryCrystallography

Abstract

fetched live from OpenAlex

Details of a large-area-compatible sub-600 °C process for the fabrication of high mobility field-effect transistors on single crystalline silicon are presented. It is shown that lift-off and solid-phase-epitaxy (SPE) can be used in conjunction with plasma enhanced chemical vapor deposition (PECVD) to create MOSFET transistors with mobilities comparable to other high-performance techniques. Furthermore, use of an amorphous silicon/silicon oxynitride (SiOxNy) sacrificial bilayer is shown to make the process capable of self-aligned silicidation. The topography of the deposition is studied using cross-sectional scanning electron microscopy (SEM). It is shown that a successful lift-off can be achieved through the proper design of the sacrificial bilayer despite the high degree of step coverage generally exhibited by PECVD films. Cross-sectional transmission electron microscopy (TEM) is used to reveal the microstructure of the epitaxy and silicidation. Current-voltage characteristics of the fabricated transistors are presented and the field effect mobility of electrons on the fabricated devices is reported. The developed process is particularly useful in applications where high mobility transistors need to be built on silicon/nonsilicon hybrid platforms that exclude high processing temperatures.

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 categoriesMeta-epidemiology (narrow)
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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.263
Teacher spread0.251 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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