Q.J. Zhang

Qi-Jun Zhang

Fellow of IEEE
Fellow of Canadian Academy of Engineering
Chancellor's Professor

Department of Electronics
Carleton University
1125 Colonel By Drive, Ottawa, Canada K1S 5B6
Tel: 613 520-2600 Ext. 5778, Fax: 613 520-5708

Email: qjz@doe.carleton.ca

Special Issue of the IEEE Transactions on Microwave Theory and Techniques (2022)

AI and Machine Learning Technologies for Microwaves

Prof. Q.J. Zhang, Carleton University, Ottawa, Canada.

Special Issue of the IEEE Microwave Magazine (2021)

Machine Learning in Microwave Engineering

Prof. Costas Sarris, University of Toronto, Toronto, Canada.
Prof. Q.J. Zhang, Carleton University, Ottawa, Canada.

Recent Invited Talks

Q.J. Zhang, “Artificial neural networks for microwave behavioral modeling,” IEEE MTT-S International Microwave Symposium, Workshop on Machine Learning and AI Techniques with Intelligent RF/mmWave Systems for Wireless Communication, Sensing, and Computation, (Atlanta, Georgia) June 2021 (invited)
Q.J. Zhang, “Machine learning and cognition-driven approaches to microwave design,” IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, (Hangzhou, China), December 2020. (invited keynote talk)
Q.J. Zhang and F. Feng, “Artificial neural networks for microwave behavioral modeling," IEEE Radio and Wireless Symposium, Workshop on Machine Learning and Dimensionality Reduction Techniques for RF Components and Systems: From Modeling to Linearization (San Antonio, Texas), January 2020 (invited).
Q.J. Zhang, “Machine learning and artificial neural networks for high-speed/high-frequency electronic design,” International Conf. on Electrical Performance of Electronic Packaging and Systems, (Montreal, Canada), October 2019 (invited Keynote talk)
Q.J. Zhang, “Artificial neural networks for microwave design, an overview,” IEEE MTT-S International Microwave Symposium, Workshop on Microwave Engineering Applications of Machine Learning: Past, Present and Future, (Boston, MA) June 2019 (invited)
Q.J. Zhang, “Artificial neural networks for microwave design - recent advances and future directions,” European Microwave Conference in Central Europe, Workshop on Advances in Smart Modeling Techniques for Microwave Engineering, (Prague, Czech Republic), May 2019 (Invited).

Invited Overview in the Inaugural Issue of IEEE Journal of Microwaves (2021)

Simulation and Automated Modeling of Microwave Circuits: State-of-the-Art and Emerging Trends

Q.J. Zhang, E. Gad, B. Nouri, W.C. Na and M.S. Nakhla

A Pioneering Book of Its Kind

Neural Networks for RF and Microwave Design from Artech House Publishers, Boston, MA, 2000.

Prof. Q.J. Zhang, Carleton University, Ottawa, Canada.
Prof. K.C. Gupta, University of Colorado, Boulder, Colorado, USA.

IEEE MTT-S International Conference on Electromagnetic and Multiphysics Modeling and Optimization (NEMO)

The NEMO conference is an annual event sponsored by the IEEE MTT Society. With Dr. Zhang as one of the founding executives, and inaugurated in 2014, the NEMO conference is now a focal point for electromagnetic- and multiphysics-based modeling, simulation and optimization for RF, microwave, terahertz, and other applications.

NEMO 2022 will be held in Limoges, France in July 2022.

Current Research
Recent Publications
History of Publications: Journal Papers and Conference Papers
Research Funding
Vacancies and Financial Aids for New Students
FAQ: Frequently Asked Questions

Current Research

Design automation technologies for design of high-speed VLSI packages, RF and microwave circuits and subsystems in wireless systems;

Machine learning and neural network technologies for high frequency electronics circuits and systems design.

Automated modeling techniques for microwave passive and active devices.

Optimization technology for electromagnetic structures, circuits and systems design.

Applications: modeling and optimization of RF and microwave circuits. Concurrent optimization techniques for signal integrity, thermal and EM based design of IC packages, multichip modules and printed circuit boards; Yield optimization, statistical modeling, manufacturability driven design of circuits;

Selected Recent Publications

Q.J. Zhang, E. Gad, B. Nouri, W.C. Na and M.S. Nakhla, “Simulation and automated modeling of microwave circuits: State-of-the-art and emerging trends,” IEEE Journal of Microwaves, vol. 1, no. 1, pp. 494-507, January 2021 (invited paper).

J. Jin, F. Feng, W. Na, J. Zhang, W. Zhang, Z.H. Zhao and Q.J. Zhang, “Advanced cognition-driven EM optimization incorporating transfer function-based feature surrogate for microwave filters,” IEEE Transactions on Microwave Theory and Techniques, vol. 69, no. 1, pp. 15-28, January 2021.

J.N. Zhang, F. Feng, J. Jin, W. Zhang, Z. Zhao and Q.J. Zhang, “Adaptively weighted yield-driven EM optimization incorporating neuro-transfer function surrogate with applications to microwave filters,” IEEE Transactions Microwave Theory and Techniques, vol. 69, no. 1, pp. 518-528, January 2021.

W. Zhang, F. Feng, J.N. Zhang, Z.H. Zhao, J.G. Ma and Q.J. Zhang, “Parallel decomposition approach to wide-range parametric modeling with applications to microwave filters,” IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 12, pp. 5288-5306, December 2020.

J.N. Zhang, F. Feng, J. Jin and Q.J. Zhang, “Efficient yield estimation of microwave structures using mesh deformation-incorporated space mapping surrogates,” IEEE Microwave and Wireless Components Letters, vol. 30, no. 10, pp. 937-940, October 2020.

F. Feng, W.C. Na, W. Liu, SX. Yan, L. Zhu and Q.J. Zhang, “Parallel Gradient-Based EM Optimization for Microwave Components Using Adjoint-Sensitivity- Based Neuro-Transfer Function Surrogate,” IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 9, pp. 3606-3620, September 2020.

Z.H. Zhao, L. Zhang, F. Feng, W. Zhang and Q.J. Zhang, “Space mapping technique using decomposed mappings for GaN HEMT modeling with trapping effects,” IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 8, pp. 3318-3341, August 2020.

F. Feng, J. Zhang, J. Jing, W. Na, S. Yan, and Q. J. Zhang, ‘’Efficient FEM-based EM optimization technique using combined Lagrangian method with Newton’s method,” IEEE Transactions on Microwave Theory Techniques, vol. 68, no. 6, pp. 2194-2205, June 2020.

J.N. Zhang, F. Feng, W. Zhang, J. Jin, J. Ma, and Q. J. Zhang, “A novel training approach for parametric modeling of microwave passive components using Pade via Lanczos and EM sensitivities,” IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 6, pp. 2215-2233, June 2020.

Z.H. Zhao, F. Feng, W. Zhang, J.N. Zhang, J, Jin and Q.J. Zhang, “Parametric modeling of EM behavior of microwave components using combined neural networks and hybrid-based transfer functions,” IEEE Access, vol. 8, pp. 93922-93938, May 2020.

J. Jin, F. Feng, J.N. Zhang, S.X. Yan, W.C. Na and Q.J. Zhang, “A novel deep neural network topology for parametric modeling of passive microwave components,” IEEE Access, vol. 8, pp. 82273-82285, May 2020.

F. Feng, W. Na, W. Liu, S. Yan, L. Zhu, J. Ma and Q.J. Zhang, “Multi-feature assisted neuro-transfer function surrogate based EM optimization exploiting trust region algorithms for microwave filter design,” IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 2, pp. 531-542, February 2020.

W. Zhang, F. Feng, S. Yan, W. Na, J. Ma and Q.J. Zhang, “EM Centric Multi-physics Optimization of Microwave Components Using Parallel Computational Approach,” IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 2, pp. 479-489, February 2020.

J. Jin, F. Feng, W.C. Na, S.X. Yan, W.Y. Liu, L. Zhu and Q.J. Zhang, “Recent advances in neural network-based inverse modeling techniques for microwave applications,” International Journal on Numerical Modeling, Special Issue in Forward and Inverse Surrogate Modeling for High-Frequency Design, vol. 33, no. 6, Nov/Dec 2020.

F. Feng, J.N. Zhang, W. Zhang, Z.H. Zhao, J. Jin and Q.J. Zhang, “Recent advances in parametric modeling of microwave components using combined neural network and transfer function,” International Journal on Numerical Modeling, Special Issue in Forward and Inverse Surrogate Modeling for High-Frequency Design, vol. 33, no. 6, Nov/Dec 2020.

J.N. Zhang, F. Feng, W. Na, S. Yan and Q.J. Zhang, “Parallel space-mapping based yield-driven EM optimization incorporating trust region algorithm and polynomial chaos expansion,” IEEE Access, vol. 7, pp. 143673-143683, 2019.

J. Jin, C. Zhang, F. Feng, W. Na, J.G. Ma and Q.J. Zhang, “Deep neural network technique for high dimensional microwave modeling and applications to parameter extraction of microwave filters,” IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 10, pp. 4140-4155, October 2019.

F. Feng, J.N. Zhang, W. Zhang, Z.H. Zhao, J. Jin and Q.J. Zhang, “Coarse and fine mesh space mapping for EM optimization incorporating mesh deformation,” IEEE Microwave and Wireless Component Letters, vol. 29, no. 8, pp. 510-512, August 2019.

A.M. Gabr, C. Featherston, C. Zhang, C. Bonfil, Q.J. Zhang and T.J. Smy, “Design and optimization of optical passive elements using artificial neural networks,” Journal of the Optical Society of America, B, vol. 36, no. 4, pp. 999-1007, April 2019.

F. Feng, C. Zhang, W. Na, J. Zhang, W. Zhang and Q. J. Zhang, “Adaptive feature zero assisted surrogate-based EM optimization for microwave filter design,” IEEE Microwave Wireless Component Letters, vol. 29, no. 1, pp. 2-4, January 2019.

W.C. Na, W.Y. Liu, L. Zhu, F. Feng, J.G. Ma and Q.J. Zhang, “Advanced extrapolation technique for neural-based microwave modeling and design,” IEEE Transactions on Microwave Theory and Techniques, vol. 66, no. 10, pp. 4397-4418, October 2018.

C. Zhang, J. Jin, W.C. Na, Q.J. Zhang and M. Yu, “Multivalued neural network inverse modeling and applications to microwave filters,” IEEE Transactions on Microwave Theory and Techniques, vol. 66, no. 8, pp. 3781-3797, August 2018.

J.N. Zhang, C. Zhang, F. Feng, W. Zhang, J.G. Ma and Q.J. Zhang, “Polynomial chaos-based approach to yield driven EM optimization,” IEEE Transactions on Microwave Theory and Techniques, vol. 66, no. 7, pp. 3186-3199, July 2018.

W. Zhang, F. Feng, V. Gongal-Reddy, J.N. Zhang, S. Yan, J.G. Ma and Q.J. Zhang, “Space mapping approach to electromagnetic centric multiphysics parametric modeling of microwave components,” IEEE Transactions on Microwave Theory and Techniques, vol. 66, no. 7, pp. 3169-3185, July 2018.

History of Publications: Journal Papers and Conference Papers

Vacancies and Financial Aids to New Students

Graduate studies towards Ph.D, or Master's degree
Post-Doctoral Fellow (require a Ph.D. degree in CAD, or microwaves or neural networks)

Financial Aids in the forms of Research Assistantships and post-doctoral fellowships are available for new applicants. These financial aids are from my research funds, and as such, you are required to actively carry out one of my research projects.

Research Funding

All research projects are fully funded by government and industry. All my graduate students and researchers receive financial support and must participate in at least one research project.

Natural Sciences and Engineering Research Council of Canada
Ontario Research Fund - Research Excellence Program
Industry: COM DEV, Blackberry, Freescale Semiconductor, Intel, Nokia, Nortel Networks, NCMS/Advanced Embedded Passive Technology Consortium
Micronet: Networks of Centers of Excellence on Microelectronic Devices, Circuits and Systems
OCE/CITO: Ontario Centers of Excellence, Communications and Information Technology Ontario