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Towards A Computer-Aided Design Tool Dedicated to Foundry Open Gate Junction Field-Effect Transistor Sensor’s Process

2024· article· en· W4402474950 on OpenAlexaff
Abbas Panahi, Sebastian Magierowski, Ebrahim Ghafar‐Zadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsYork University
Fundersnot available
KeywordsFoundryTransistorProcess (computing)Computer scienceField-effect transistorProcess designField (mathematics)Electrical engineeringEngineeringElectronic engineeringEmbedded systemWork in processMechanical engineeringVoltage

Abstract

fetched live from OpenAlex

This paper underscores the necessity of creating a user-friendly computer-aided design interface application tailored for the design and simulation of a standard foundry silicon-based transistor called the open-gate junction field-effect transistor (OG-JFET). Leveraging fabricated chip and characterization results, the simulation application is developed using the COMSOL Application Builder. This significantly streamlines the simulation and analysis process, particularly for individuals with limited expertise in electrical engineering and semiconductor devices. Constructing the application within COMSOL Multiphysics offers enhanced adaptability in comprehending and crafting biosensors for prospective users of this technology. This proves vital given the multifaceted demands of biosensor design, which encompass various physics domains such as solution properties, electrical fields, magnetic fields, and charge interactions—all facilitated by the multiphysics nature of COMSOL. These aspects are not readily accessible through other semiconductor simulation software platforms like TCAD. The results of a single channel OG-JFET with a channel length of 5 µm with p-type thickness of 1.6 µm is compared to the experiment showing perfect agreement in the 0.5 < Vgs < 0.8 range and Vds = 1 and 2 with minimum mismatch.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.982

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)0.0010.000

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 head0.028
GPT teacher head0.295
Teacher spread0.267 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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