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Record W2548244308 · doi:10.1109/iedm.2005.1609315

Integration and optimization of embedded-sige, compressive and tensile stressed liner films, and stress memorization in advanced SOI CMOS technologies

2005· article· en· W2548244308 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsPMOS logicNMOS logicMaterials scienceCMOSStress (linguistics)Silicon on insulatorStrain engineeringOptoelectronicsTransistorUltimate tensile strengthCompressive strengthElectronic engineeringElectrical engineeringComposite materialSiliconEngineeringVoltage

Abstract

fetched live from OpenAlex

An optimized 4-way stress integration on partially-depleted SOI (PD-SOI) CMOS is presented. An embedded-SiGe process and a compressive-stressed liner film are used to induce compressive strain in the PMOS (PMOS "stressors"). A stress memorization process and a tensile-stressed liner film are used to induce tensile strain in the NMOS (NMOS "stressors"). With optimization, the different stress techniques are highly compatible and additive to each other, improving PMOS and NMOS saturation drive current by 53% and 32%, respectively. This improvement results in 40% higher product speed. To demonstrate the extendibility for future transistor nodes the stress improvements were increased further resulting in record PMOS performance of IDSAT=860muA/mum at 200nA IOFF (self-heating corrected) and 1V. The stress techniques are proven in AMD's 90nm manufacturing processes, and have been scaled for use in 65nm manufacturing

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.453

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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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