Characteristics Prediction and Optimization of GaN CAVET Using a Novel Physics-Guided Machine Learning Method.

Wenbo Wu, Jie Wang, Jiangtao Su, Zhanfei Chen, Zhiping Yu

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Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Wenbo WuInnovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

Jie WangInnovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou 310018, China.ORCID 0000-0002-4635-8230

Jiangtao SuInnovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

Zhanfei ChenNingbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo 315200, China.

Zhiping YuSchool of Integrated Circuits, Tsinghua University, Beijing 100084, China.ORCID 0000-0001-8701-8438

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a physics-guided machine learning (PGML) approach to model the I-V characteristics of GaN current aperture vertical field effect transistors (CAVET). By adopting the method of transfer learning and the shortcut structure, a physically guided neural network model is established. The shallow neural network with tanh as the basis function is combined with a hypernetwork that dynamically generates its weight parameters. The influence of transconductance is added to the loss function. This model can synchronously predict the output and transfer characteristics of the device. Under the condition of small samples, the prediction error is controlled within 5%, and the R2 value reaches above 0.99. The proposed PGML approach outperforms conventional approaches, ensuring physically meaningful and robust predictions for device optimization and circuit-level simulations.

Indexed as

current aperture vertical electron transistorhypernetworkphysics-guided artificial neural networkphysics-guided machine learningshallow neural networkshortcutTCAD

Identifiers

PMID41011895

DOI10.3390/mi16091005

PMCPMC12471978

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.