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Comparative Thermal Analysis of Single vs. Arrayed Layouts for Integrated Power Transistors Using COMSOL

2025· article· W4417169111 on OpenAlexaff
Mostafa Amer, Ahmed Abuelnasr, Ahmad Hassan, Y. Savaria

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTransistorPower semiconductor devicePower electronicsTransistor modelPower (physics)ThermalMultiple-emitter transistorIntegrated circuitStatic induction transistor

Abstract

fetched live from OpenAlex

Monolithic power electronics are a key solution for compact and efficient industrial systems. However, in some applications, integrated power transistors must withstand high voltage and current stresses, especially during short-circuit faults, until protection features activate. This necessitates careful design, layout optimization, and advanced thermal modeling to ensure reliability. This work proposes an arrayed-layout approach, where a MOSFET power transistor is divided into multiple parallel instances to enhance thermal performance of the silicon chip. The effects of power dissipation (P), number of transistor instances (N), and inter-instance spacing (Δ) on steady-state and transient thermal behavior are analyzed using finite-element analysis in COMSOL. Results show that transitioning from a single-instance transistor (N=1) to a multi-instance transistor (N>1) using the proposed approach while optimizing Δ can reduce the maximum surface temperature by up to 12.5°C and 30°C (10% reduction) at 1W and 2.5W power dissipation, respectively. In addition, the time required to reach a critical temperature (e.g., 125°C) improves by up to 64%, delaying thermal runaway. These findings highlight the thermal benefits of arrayed transistor layouts, providing valuable insights for optimizing integrated power transistor design.

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 categoriesMeta-epidemiology (narrow)
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 score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.041
GPT teacher head0.290
Teacher spread0.249 · 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.

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
Published2025
Admission routes1
Has abstractyes

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