𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Modeling the Time-Harmonic Electromagnetic Emissions of Microwave Circuits

✍ Scribed by Ramanujan, A.; Riah, Z.; Louis, A.

Book ID
114625025
Publisher
IEEE
Year
2012
Tongue
English
Weight
1018 KB
Volume
54
Category
Article
ISSN
0018-9375

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Time-domain 3D electromagnetic model for
Time-domain 3D electromagnetic model for hybrid and monolithic microwave and optoelectronic integrated circuits
✍ F. A. Mohammadi; M. C. E. Yagoub; K. Raahemifar 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 275 KB

## Abstract This paper presents an accurate and robust 3D time‐domain electromagnetic model for hybrid and monolithic microwave and optoelectronic integrated circuits. The time‐domain model has been validated for different structures, such as metallic waveguides and planner lines under harmonic‐osc

Gradient-based optimization of packaged
Gradient-based optimization of packaged integrated microwave circuits exploiting reduced electromagnetic models
✍ Atousa Assadihaghi; Stéphane Bila; Dominique Baillargeat; Michel Aubourg; Serge 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 143 KB

## Abstract A gradient‐based optimization method is presented for the design of packaged integrated microwave circuits. The method uses a reduced electromagnetic model resulting in a quasi‐instantaneous analysis of the packaged device. A gradient‐based strategy identifying parasitic effects due to

An accurate wavelet neural-network-based
An accurate wavelet neural-network-based model for electromagnetic optimization of microwave circuits
✍ S. Bila; Y. Harkouss; M. Ibrahim; J. Rousset; E. N'Goya; D. Baillargeat; S. Verd 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 246 KB 👁 2 views

In this paper, a relevant automated electromagnetic (EM) optimization method and a novel, fast, and accurate artificial neural network are proposed for the efficient CAD modeling of microwave circuits. We lay the groundwork for our investigation of radial wavelet neural networks WNNs trained by BFGS