Zegen Zhu;Gianni Bosi;Antonio Raffo;Giovanni Crupi;Jialin Cai
{"title":"面向 S22 和 h21 扭结效应分析的 GaN HEMT 准确建模:一种有效的机器学习方法","authors":"Zegen Zhu;Gianni Bosi;Antonio Raffo;Giovanni Crupi;Jialin Cai","doi":"10.1109/JEDS.2024.3364809","DOIUrl":null,"url":null,"abstract":"In this work, for the first time, a machine learning behavioral modeling methodology based on gate recurrent unit (GRU) is developed and used to model and then analyze the kink effects (KEs) in the output reflection coefficient \n<inline-formula> <tex-math>$(S_{22})$ </tex-math></inline-formula>\n and the short-circuit current gain \n<inline-formula> <tex-math>$(h_{21})$ </tex-math></inline-formula>\n of an advanced microwave transistor. The device under test (DUT) is a 0.25-\n<inline-formula> <tex-math>$\\mu \\text{m}$ </tex-math></inline-formula>\n gallium nitride (GaN) high electron mobility transistor (HEMT) on silicon carbide (SiC) substrate, which has a large gate periphery of 1.5 mm. The scattering (S-) parameters of the DUT are measured at a frequency up to 65 GHz and at an ambient temperature up to 200°C. The proposed model can accurately reproduce the KEs in \n<inline-formula> <tex-math>$S_{22}$ </tex-math></inline-formula>\n and in \n<inline-formula> <tex-math>$h_{21}$ </tex-math></inline-formula>\n, enabling an effective analysis of their dependence on the operating conditions, bias point and ambient temperature. It is worth noticing that the proposed transistor model shows also good performance in both interpolation and extrapolation test.","PeriodicalId":13210,"journal":{"name":"IEEE Journal of the Electron Devices Society","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433010","citationCount":"0","resultStr":"{\"title\":\"Accurate Modeling of GaN HEMTs Oriented to Analysis of Kink Effects in S22 and h21: An Effective Machine Learning Approach\",\"authors\":\"Zegen Zhu;Gianni Bosi;Antonio Raffo;Giovanni Crupi;Jialin Cai\",\"doi\":\"10.1109/JEDS.2024.3364809\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, for the first time, a machine learning behavioral modeling methodology based on gate recurrent unit (GRU) is developed and used to model and then analyze the kink effects (KEs) in the output reflection coefficient \\n<inline-formula> <tex-math>$(S_{22})$ </tex-math></inline-formula>\\n and the short-circuit current gain \\n<inline-formula> <tex-math>$(h_{21})$ </tex-math></inline-formula>\\n of an advanced microwave transistor. The device under test (DUT) is a 0.25-\\n<inline-formula> <tex-math>$\\\\mu \\\\text{m}$ </tex-math></inline-formula>\\n gallium nitride (GaN) high electron mobility transistor (HEMT) on silicon carbide (SiC) substrate, which has a large gate periphery of 1.5 mm. The scattering (S-) parameters of the DUT are measured at a frequency up to 65 GHz and at an ambient temperature up to 200°C. The proposed model can accurately reproduce the KEs in \\n<inline-formula> <tex-math>$S_{22}$ </tex-math></inline-formula>\\n and in \\n<inline-formula> <tex-math>$h_{21}$ </tex-math></inline-formula>\\n, enabling an effective analysis of their dependence on the operating conditions, bias point and ambient temperature. It is worth noticing that the proposed transistor model shows also good performance in both interpolation and extrapolation test.\",\"PeriodicalId\":13210,\"journal\":{\"name\":\"IEEE Journal of the Electron Devices Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433010\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of the Electron Devices Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10433010/\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of the Electron Devices Society","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10433010/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Accurate Modeling of GaN HEMTs Oriented to Analysis of Kink Effects in S22 and h21: An Effective Machine Learning Approach
In this work, for the first time, a machine learning behavioral modeling methodology based on gate recurrent unit (GRU) is developed and used to model and then analyze the kink effects (KEs) in the output reflection coefficient
$(S_{22})$
and the short-circuit current gain
$(h_{21})$
of an advanced microwave transistor. The device under test (DUT) is a 0.25-
$\mu \text{m}$
gallium nitride (GaN) high electron mobility transistor (HEMT) on silicon carbide (SiC) substrate, which has a large gate periphery of 1.5 mm. The scattering (S-) parameters of the DUT are measured at a frequency up to 65 GHz and at an ambient temperature up to 200°C. The proposed model can accurately reproduce the KEs in
$S_{22}$
and in
$h_{21}$
, enabling an effective analysis of their dependence on the operating conditions, bias point and ambient temperature. It is worth noticing that the proposed transistor model shows also good performance in both interpolation and extrapolation test.
期刊介绍:
The IEEE Journal of the Electron Devices Society (J-EDS) is an open-access, fully electronic scientific journal publishing papers ranging from fundamental to applied research that are scientifically rigorous and relevant to electron devices. The J-EDS publishes original and significant contributions relating to the theory, modelling, design, performance, and reliability of electron and ion integrated circuit devices and interconnects, involving insulators, metals, organic materials, micro-plasmas, semiconductors, quantum-effect structures, vacuum devices, and emerging materials with applications in bioelectronics, biomedical electronics, computation, communications, displays, microelectromechanics, imaging, micro-actuators, nanodevices, optoelectronics, photovoltaics, power IC''s, and micro-sensors. Tutorial and review papers on these subjects are, also, published. And, occasionally special issues with a collection of papers on particular areas in more depth and breadth are, also, published. J-EDS publishes all papers that are judged to be technically valid and original.