Parasitic parameters extraction of InP HBT only from S-parameters measured under cut-off and normal bias conditions

Author:

Qi Junjun,Lu Hongliang,Zhao Ranran,Yan Silu,Cheng Lin,Zhang Yuming

Funder

Shanxi Provincial Key Research and Development Project

Publisher

Elsevier BV

Subject

Materials Chemistry,Electrical and Electronic Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Reference26 articles.

1. Terahertz signal source and receiver operating near 600 GHz and their 3-D imaging application;Kim;IEEE Trans Microw Theor Technol,2021

2. 80-110-GHz broadband linear PA with 33% peak PAE and comparison of stacked common base and common emitter PA in InP;Liu;IEEE Microw Wireless Compon Lett,2021

3. Machine learning technique based extremely broadband small signal behavioral model for InP DHBTs;Cai;Int J RF Micro CAE,2021

4. An improved transistor modeling methodology exploiting the quasi-static approximation;Jarndal;IEEE J Electron Device,2021

5. An improved nonlinear model for millimeter-wave InP HBT including DC/AC dispersion effects;Zhang;IEEE Microw Wireless Compon Lett,2021

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1. Knowledge-Guided Neural Network based Nonlinear Current Model with Combined Loss Function;2024 2nd International Symposium of Electronics Design Automation (ISEDA);2024-05-10

2. A Knowledge-Guided Neural Network-Based Large-Signal Model for InP HBTs With Self-Heating Effect Consideration;IEEE Transactions on Microwave Theory and Techniques;2024

3. An Aging Small-Signal Equivalent Circuit Modeling Method for InP HBT;IEEE Transactions on Device and Materials Reliability;2023-12

4. Small-signal behavioral-level modeling of InP HBT based on SO-BP neural network;Solid-State Electronics;2023-11

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