Dr. J. E. Rayas Sánchez

Optimization-Based Modeling and Design of Electronic Circuits (graduate course, 2019)

This course will enable students to apply advanced numerical optimization techniques to modeling and design of electronic circuits. Students will gain basic skills on fundamental classical optimization methods, as well as on some state-of-the-art engineering optimization techniques. Students will implement some of these techniques and apply them to electronic circuits for design optimization and parameter extraction and surrogate modeling. Applications to low and high-frequency electronic circuits will be considered, including electromagnetics-based modeling and design. The use of numerical software and commercially available electronic circuit CAD tools for hands-on experience will be emphasized throughout the course.

Syllabus

Lectures

1. A review on matrix computations  (1)  (2)  (3)

2. Introduction to numerical optimization  (1)  (2)  (3)

3. Unidimensional search methods  (1)  (2)

4. Unconstrained multidimensional methods  (1)  (2)

5. Constrained multidimensional methods  (1)  (2)  (3)

6. Solving systems of nonlinear equations  (1)

7. Circuit design using classical optimization methods (1)  (2)  (3)  (4)  (5)

8. Circuit parameter extraction using classical optimization methods (1)  (2)

9. Space mapping techniques  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)

10. An introduction to surrogate modeling

11. Modeling and design of electronic circuits using artificial neural networks  (1)  (2)  (3)  (4) 

12. Neural space mapping methods for modeling and design  (1)  (2)  (3)  (4)

Assignments

        Assignment 1

        Assignment 2

        Assignment 3  (test functions)

        Assignment 4  (target data files)

        Excercise on SM  (prob1)  (prob2)  (prob3)

Suggested Final Projects

Historical List of Final Projects

Final Projects (Schedule)

Guidelines for the Final Project Presentation and Written Report

Grades

Some References

P. E. Frandsen, K. Jonasson, H. B. Nielsen and O. Tingleff, Unconstrained Optimization. Lyngby, Denmark: Department of Mathematical Modeling, Technical University of Denmark, 1999.

J. E. Rayas-Sánchez and V. Gutiérrez-Ayala, “EM-based Monte Carlo analysis and yield prediction of microwave circuits using linear-input neural-output space mapping,” IEEE Trans. Microwave Theory Tech., vol. 54, pp. 4528-4537, Dec. 2006.

J. E. Rayas-Sánchez, “Power in simplicity with ASM: tracing the aggressive space mapping algorithm over two decades of development and engineering applications,” IEEE Microwave Magazine, vol. 17, no. 4, pp. 64-76, Apr. 2016.

J. E. Rayas-Sánchez, F. Lara-Rojo and E. Martínez-Guerrero, “A linear inverse space mapping (LISM) algorithm to design linear and nonlinear RF and microwave circuits,” IEEE Trans. Microwave Theory Tech., vol. 53, pp. 960-968, Mar. 2005.

J. E. Rayas-Sánchez, “EM-based optimization of microwave circuits using artificial neural networks: the state of the art,” IEEE Trans. Microwave Theory Tech., vol. 52, pp. 420-435, Jan. 2004.

J. W. Bandler, M. A. Ismail, J. E. Rayas-Sánchez and Q. J. Zhang, “Neural inverse space mapping for EM-based microwave design,” Int. J. RF and Microwave CAE, vol. 13, pp. 136-147, Mar. 2003.

J. W. Bandler, J. E. Rayas-Sánchez and Q. J. Zhang, “Yield-driven electromagnetic optimization via space mapping-based neuromodels,” Int. J. RF and Microwave CAE, vol. 12, pp. 79-89, Jan. 2002.

M. H. Bakr, J. W. Bandler, M. A. Ismail, J. E. Rayas-Sánchez and Q. J. Zhang, “Neural space mapping optimization for EM-based design,” IEEE Trans. Microwave Theory Tech., vol. 48, pp. 2307-2315, Dec. 2000.

J. W. Bandler, M. A. Ismail, J. E. Rayas-Sánchez and Q. J. Zhang, “Neuromodeling of microwave circuits exploiting space mapping technology,” IEEE Trans. Microwave Theory Tech., vol. 47, pp. 2417-2427, Dec. 1999.

J. E. Rayas-Sánchez, Neural Space Mapping Methods for Modeling and Design of Microwave Circuits, Ph.D. Thesis, McMaster University, Hamilton, Canada L8S 4K1, 2001.