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Record W1590842594 · doi:10.1109/iedm.2002.1175825

FinFET scaling to 10 nm gate length

2003· article· en· W1590842594 on OpenAlexaff
Bin Yu, Leland Chang, Safayet Ahmed, Haihong Wang, Suzanne O. Bell, Chih-Yuh Yang, Cyrus Tabery, C. K. F. Ho, Qian Xiang, Tsu-Jae King, Jeffrey Bokor, Chenming Hu, Ming-Ren Lin, David F. Kyser

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsTransconductanceMaterials scienceCMOSMOSFETOptoelectronicsPlanarShort-channel effectFabricationTransistorChannel (broadcasting)Design for manufacturabilityLogic gateScalingElectrical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

While the selection of new "backbone" device structure in the era of post-planar CMOS is open to a few candidates, FinFET and its variants show great potential in scalability and manufacturability for nanoscale CMOS. In this paper we report the design, fabrication, performance, and integration issues of double-gate FinFETs with the physical gate length being aggressively shrunk down to 10 nm and the fin width down to 12 nm. These MOSFETs are believed to be the smallest double-gate transistors ever fabricated. Excellent short-channel performance is observed in devices with a wide range of gate lengths (10/spl sim/105 nm). The observed short-channel behavior outperforms any reported single-gate silicon MOSFETs. Due to the [110] channel crystal orientation, hole mobility in the fabricated p-channel FinFET exceeds greatly that in a traditional planar MOSFET. At 105 nm gate length, the p-channel FinFET shows a record-high transconductance of 633 /spl mu/S//spl mu/m at a V/sub dd/ of 1.2 V. Working CMOS FinFET inverters are also demonstrated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.222
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations595
Published2003
Admission routes1
Has abstractyes

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