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Advanced Annealing Schemes for High-Performance SOI Logic Technologies

2008· article· en· W2016396677 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

VenueMaterials science forum · 2008
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsMaterials scienceAnnealing (glass)Silicon on insulatorTransistorOptoelectronicsScalingLogic gateNode (physics)NanotechnologyElectronic engineeringEngineering physicsElectrical engineeringSiliconEngineeringVoltage

Abstract

fetched live from OpenAlex

We have extensively studied the impact of advanced annealing schemes for highperformance SOI logic technologies. Starting with the 130 nm technology node, we introduced spike rapid thermal annealing (sRTA). Continuous temperature reduction combined with implant scaling helped to improve transistor performance and short channel behavior. During the development of the 90 nm technology we evaluated flash lamp and laser annealing (FLA). These techniques became an essential part of the 65 nm node. At this node we also faced major challenges in terms of compatibility with new materials like SiGe as well as the need for reduction of process parameter fluctuations. Scaling will be continued with the 45 nm technology node towards a truly diffusionless process.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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