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Record W2162827367 · doi:10.5555/832284.835426

T3: Trends and Challenges in VLSI Technology Scaling towards 100nm

2002· article· en· W2162827367 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

VenueAsia and South Pacific Design Automation Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTransistorVery-large-scale integrationComputer scienceSkewElectronic engineeringNode (physics)Transistor countSilicon on insulatorJitterElectrical engineeringEngineeringMaterials scienceTelecommunicationsSiliconVoltageOptoelectronics

Abstract

fetched live from OpenAlex

Summary form only given. Moore's Law drives VLSI technology to continuous increases in transistor densities and higher clock frequencies. This tutorial will review the trends in VLSI technology scaling in the last few years and discuss the challenges facing process and circuit engineers in the 100nm generation and beyond. The first focus area is the process technology, including transistor scaling trends and research activities for the 100nm technology node and beyond. The transistor leakage and interconnect RC delays will continue to increase. The tutorial will review new circuit design techniques for emerging process technologies, including dual Vt transistors and silicon-on-insulator. It will also cover circuit and layout techniques to reduce clock distribution skew and jitter, model and reduce transistor leakage and improve the electrical performance of flip-chip packages. Finally, the tutorial will review the test challenges for the 100nm technology node due to increased clock frequency and power consumption (both active and passive) and present several potential solutions.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.770

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 head0.092
GPT teacher head0.240
Teacher spread0.148 · 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