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Record W2990319948 · doi:10.18280/ejee.210502

An Approach to Manufacture Small Multiplexer with Dense Field-Effect Transistors

2019· article· en· W2990319948 on OpenAlexvenueno aff
E. L. Pankratov

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsnot available
Fundersnot available
KeywordsMultiplexerField-effect transistorField (mathematics)TransistorOptoelectronicsMaterials scienceMultiplexingElectrical engineeringComputer scienceTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper introduces an approach to manufacture a small multiplexer with dense field-effect transistors.First, a framework of two-level current-mode logic gates was designed for the transistors.The logic gates are organized heterogeneously with a substrate, epitaxial layers and buffer layer.Considering the different properties of materials in the framework, the density of transistors was improved by annealing dopant and/or radiation defects, thus reducing the size of the multiplexer.In addition, the mismatch-induced stress was alleviated through ion implantation.Next, an analytical approach was developed to analyze the mass and heat transport in the heterogenous framework during the production of integrated circuits, in the presence of mismatch-induced stress.The proposed method makes it possible to capture both nonlinear variations of parameters in space and time through the production 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.

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)0.000
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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