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Record W2021269737 · doi:10.1002/pssc.200405135

Beyond microelectronics: materials and technology for nano-scale CMOS devices

2004· article· en· W2021269737 on OpenAlexafffund
D. A. Buchanan

Bibliographic record

VenuePhysica status solidi. C, Conferences and critical reviews/Physica status solidi. C, Current topics in solid state physics · 2004
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Manitoba
FundersNational Research Council CanadaNational Science CouncilCanada Research Chairs
KeywordsCMOSMicroelectronicsMaterials scienceTransistorOptoelectronicsMetal gateNanotechnologyLeakage (economics)Engineering physicsFabricationElectron mobilitySiliconScalingField-effect transistorGate dielectricElectrical engineeringCapacitanceTinGate oxideChemistryEngineeringVoltageElectrodeMetallurgy

Abstract

fetched live from OpenAlex

The scaling of CMOS devices into the nanometer regime has required the introduction of new materials in field effect transistor fabrication. Foremost of these is the introduction or new gate dielectrics to replace the ultra-thin SiO2 films traditionally used. It has been found that these materials, such as ZrO2, HfO2 and Al 2O3, reduce gate leakage currents by 2–5 orders of magnitude. Unfortunately these new high-κ materials have also resulted in substantial electron and hole mobility reduction. However, with the introduction of strained silicon, in which mobilities have been recovered to the extent that even with the high-κ gate dielectrics improvements have been noted approaching 60%. Further increases in electron and more importantly hole mobilities have been discovered with the use of silicon substrates of either (110) or (111) orientations. However, the electron mobility had a significant decrease with the same substrates. Finally the introduction of metal gates has allowed scaling to continue by eliminating the capacitance of the poly-Si gate. Even though the requirements for such metal gates are quite restrictive, a number of candidates have been found. Such metal systems that might be used include particular Ru-Ta alloys gates for both nFETs and pFETs can be fabricated and perform as desired. Similarly, the use of a variety of nitrogen concentrations in TiN and TaN yields a range of acceptable work functions which might be applicable to either or both FET types. The introduction of these materials increases device performance but it is their integration into CMOS processes that will determine their utility. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.317
Teacher spread0.291 · 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 designTheoretical or conceptual
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

Citations5
Published2004
Admission routes2
Has abstractyes

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