• Ph.D., in Electrical and Computer Engineering (ECE), University of Illinois at Urbana-Champaign, 2011

• M.S., in ECE, University of California at Davis, 2006

• B.S., in Electrical Engineering, University of Washington at Seattle, 1996

Brief biography:

Professor Zadehgol (SM'16 - M'07) received the B.S. degree in electrical engineering from the University of Washington, in 1996, the M.S. degree in electrical and computer engineering (ECE) from the University of California at Davis, in 2006, and the Ph.D. degree in ECE from the University Illinois at Urbana/Champaign, in 2011. With more than a decade of experience in advanced microelectronics industry, he joined the University of Idaho in 2014, where he is currently an Associate Professor of ECE and the director of the Applied Computational Electromagnetics and Signal/Power Integrity (ACEM-SPI) group. His research interests include computational electromagnetics and its various applications in low-frequency to THz electronic systems which are often disparately multi-scaled and stochastic. His work has been recognized through research grants from the National Science Foundation, NASA, Micron Technology Inc., and Schweitzer Engineering Laboratories, the IEEE TCPMT best poster-paper award, and University of Idaho's Presidential Mid-Career Award. Dr. Zadehgol is a senior member of IEEE, and a professional engineer licensed in Idaho State.

Summary of my research group's main contributions to science and engineering: 

The common theme across my group's major contributions to science lies in the development of innovative computational methodologies and algorithms for modeling and numerical characterization (simulation) of electrical devices, circuits, systems and networks. We have focused on creating efficient, accurate, and scalable solutions for disparately multi-scaled and stochastic systems in electrical engineering across the frequency spectrum; some examples include: (a) numerical simulation of stochastic loss at 100s of THz in optical interconnects exhibiting random surface roughness, (b) fault detection in air-core reactors and simulation of electromagnetic fields in solenoids at 10s of Hz, and (c) rational system characterization and macromodeling of microelectronic interconnects.

My group's overall contribution to science can be summarized as the advancement of computational algorithms and techniques that provide a deeper understanding of complex electrical systems. We have developed a series of tools that allow for more efficient simulations, predictive modeling, and fault analysis, all of which are critically important for both academic research and industry applications. Our research impacts how complex electrical systems can be modeled, understood, and optimized, bringing substantial improvements to the speed, accuracy, and resource-efficiency of these systems. 

Below are the top-five areas of our contributions to science and engineering: 

1. Advancements in Computational Electromagnetic Modeling Through Enhanced Techniques

One of our contributions to computational electromagnetics is the development of advanced methodologies for enhancing the accuracy and efficiency of finite-difference time-domain (FDTD) simulations. Starting with our 2011 work on isotropic spatial filters, we've created solutions to suppress spurious noise, making simulations more reliable across 2D and 3D spaces. Subsequent works in 2016 and 2017 focused on model order reduction techniques, introducing both deterministic and stochastic reduced-order macro-models that significantly improved the broadband radiation-field computation and accounted for uncertainties in material and structural elements. Another groundbreaking work introduced an impedance transfer function formulation that expanded the scope of materials that could be modeled, including those with both electric and magnetic conductivity. Collectively, these innovations have elevated the computational capabilities in electromagnetics, enabling more accurate, versatile, and efficient simulations across a wide range of applications. 

a.     Zadehgol A. An Impedance Transfer Function Formulation for Reduced-Order Macromodels of Sub-gridded Regions in FDTD. IEEE Transactions on Antennas and Propagation. 2017; 65(1):401-404.

b.     Zadehgol A. Stochastic Reduced-Order Electromagnetic Macromodels in FDTD. IEEE Transactions on Antennas and Propagation. 2016; 64(8):3496-3508.

c.     Zadehgol A. Deterministic Reduced-Order Macromodels for Computing the Broadband Radiation-Field Pattern of Antenna Arrays in FDTD. IEEE Transactions on Antennas and Propagation. 2016; 64(6):2418-2430.

d.     Zadehgol A, Cangellaris A. Isotropic Spatial Filters for Suppression of Spurious Noise Waves in Sub-Gridded FDTD Simulation. IEEE Transactions on Antennas and Propagation. 2011; 59(9):3272-3279.

2. Innovations in Deep Learning and Machine Learning for Accelerating Electromagnetic Modeling and Simulations

Another contribution we have made to the field of computational electromagnetics focuses on the integration of deep learning and machine learning methodologies to revolutionize traditional computational techniques. Our 2022 paper introduced a deep neural network to accurately estimate transmission line parameters in microstrip and strip-line PCB designs, achieving a significant increase in computational speed compared to traditional field solvers. In 2023, we expanded this work by employing machine learning for identifying waveguide modes from noisy 2D modal patterns with remarkable accuracy, and by developing deep neural network models for efficient electromagnetic compatibility assessment of shielding enclosures and for rapid extraction of scattering parameters in 3D discretized structures. These innovations have set a new standard for computational speed and accuracy, allowing for thousands of iterations to be performed in minimal time and computational cost, thereby accelerating the design and validation cycles in high-speed and high-bandwidth electronics. 

a.     Choupanzadeh R, Zadehgol A. A Deep Neural Network Modeling Methodology for Efficient EMC Assessment of Shielding Enclosures using MECA-Generated RCS Training Data. IEEE Transactions on Electromagnetic Compatibility. 2023.

b.     Guiana B, Zadehgol A. Machine Learning for Rectangular Waveguide Mode Identification, Using 2D Modal Field Patterns. Boulder, CO: IEEE; c2023.

c.     Newberry S, Zadehgol A. A Novel Deep Neural Network Methodology for Fast Scattering-Parameter Extraction of Discretized Structures in Three-Dimensional Space. TechRxiv. 2023. DOI: 10.36227/techrxiv.22814807

d.     Newberry S, Zadehgol A. A Deep Neural Network Modeling Methodology for Extraction of RLGC Parameters in µ-wave and mm-wave Transmission Lines. IEEE; c2022. DOI: 10.1109/EMCSI39492.2022.9889553

3. Pioneering Contributions to Modeling and Characterization of Scattering Losses in Nano-Scale SOI Waveguides

Another contribution we've made to the field of optics and opto-electronic interconnects lies in the comprehensive modeling and characterization of scattering losses in nano-scale silicon-on-insulator (SOI) waveguides. Our research advances previous work by introducing stochastic models that account for surface roughness and its impact on signal degradation in high-frequency regimes. Our 2021 paper provided an analytical solution for scattering loss in dielectric slab waveguides, presenting a groundbreaking method for computing the upper-bound value of scattering loss and significantly improving upon existing models. Further, we introduced novel techniques for extracting scattering parameters using Finite-Difference Time-Domain (FDTD) simulations, as evidenced by our subsequent papers in 2021 and 2022. My group released the open-source software optical interconnect designer tool (OIDT) to foster collaborative research in this critical area. These contributions offer novel methods and tools for the design optimization of optical and opto-electronic interconnects, particularly in high-speed, high-bandwidth applications. 

a.     Guiana B, Zadehgol A. Characterizing THz Scattering Loss in Nano-Scale SOI Waveguides Exhibiting Stochastic Surface Roughness with Exponential Autocorrelation. Electronics. 2022; 11(3). DOI: 10.3390/electronics11030307

b.     Guiana B, Zadehgol A. Stochastic Loss in Dielectric Slab Waveguides due to Exponential and Uncorrelated Surface Roughness. c2022. DOI: 10.23919/USNC-URSI52669.2022.9887412

c.     Zadehgol A. Complex s-Plane Modeling and 2D Characterization of the Stochastic Scattering Loss in Symmetric Dielectric Slab Waveguides Exhibiting Ergodic Surface-Roughness with an Exponential Autocorrelation Function. IEEE Access. 2021; :1-1. DOI: 10.1109/access.2021.3092635

d.     Guiana B, Zadehgol A. S-Parameter Extraction Methodology in FDTD for Nano-Scale Optical Interconnects. Nis, Serbia; c2021.

4. Innovating Macromodeling Techniques for Microelectronic Systems

One of our contributions to the field of electrical engineering and computational modeling has been the development of novel methodologies for system-level characterization and macromodeling of microelectronic packages and interconnects. Our work in this area has evolved progressively, offering both fundamental insights and computational advantages. Our 2015 paper presented a cellular approach to rational system characterization, translating complex frequency-domain behaviors into equivalent circuits, thereby facilitating a deeper understanding of system-level attributes such as passivity, causality, and stability. Building upon this, our 2016 work introduced the Pole Residue Equivalent System Solver (PRESS) and its more efficient successor, Coarse-to-fine Malleable Pole/Residue Equivalent System Solver (COMPRESS). These innovative approaches enable the transformation of frequency-domain models into lumped equivalent circuits, thereby accelerating the time-domain transient simulations essential for assessing signal and power integrity in electronic systems. Finally, our 2018 paper presented Parallel, Optimized, Error Maxima-Agnostic, Pole Residue Equivalent System Solver (POEMPRESS), which improved upon the fitting accuracy of previous methods while dramatically reducing computational complexity through parallelization. 

a.     Choupanzadeh R, Zadehgol A. Stability, Causality, and Passivity Analysis of Canonical Equivalent Circuits of Improper Rational Transfer Functions with Real Poles and Residues. IEEE Access. 2020; 8:125149-125162. DOI: 10.1109/ACCESS.2020.3007854

b.     Avula V, Mahanta P, Zadehgol A. Parallel, Optimized, Error Maxima-Agnostic, Pole Residue Equivalent System Solver. IEEE Transactions on Components, Packaging and Manufacturing Technology. 2018; 8(1):5-12.

c.     Avula V, Zadehgol A. Coarse-to-fine malleable Pole/Residue Equivalent System Solver. c2016.

d. Avula V, Zadehgol A. Pole residue equivalent system solver (PRESS). c2016. DOI: 10.1109/SaPIW.2016.7496258

5. Pioneering Advances in Solenoid Modeling for Diverse Applications including Critical Medical Systems

One of our contributions to electrical engineering and electromagnetism is the development of sophisticated and efficient models for analyzing and simulating the behavior of solenoids, which have wide-ranging applications, including critical roles in medical systems. Solenoids are integral to medical devices such as MRI machines for high-resolution imaging, implantable drug delivery systems for controlled medication release, electromagnetic surgical instruments for minimally invasive procedures, magnetic hyperthermia devices for targeted cancer treatment, and transcranial magnetic stimulation systems for neuromodulation and mental health treatment. Starting with our 2020 paper, we pioneered a numerical methodology using Finite-Difference Time-Domain (FDTD) simulations to remotely sense inter-turn fault events in solenoids. This methodology drastically reduced the computational burden, enabling accurate and fast simulations on typical laptop computers within minutes, as opposed to days with traditional methods. Building on this work, our 2021 paper introduced an analytical model for approximating 3-D magneto quasi-static (MQS) fields in cylindrical solenoids with helical winding, providing unprecedented insights into the magnetic field properties and flux linkages. In our most recent work in 2023, we advanced this model to efficiently compute dynamic electromagnetic fields, complex inductance, and radiation resistance across a wide range of frequencies, from low-frequency power systems to high-frequency mm-wave communication systems. Our research has laid the foundation for next-generation solenoid designs that are not only more reliable but also more versatile, promising advancements in areas ranging from power systems to telecommunications and medical systems. 

a.     Zadehgol A. Analytical Model of 3-D Helical Solenoids for Efficient Computation of Dynamic EM Fields, Complex Inductance, and Radiation Resistance. IEEE Transactions on Electromagnetic Compatibility. 2023; :1-11. DOI: 10.1109/TEMC.2023.3298886

b.     Zadehgol A. A Parametric Integral Formulation to Approximate the Magneto Quasi-Static Fields of 3-D Cylindrical Solenoids with Helical Winding. IEEE Transactions on Magnetics. 2022; 58(7):1-13. DOI: 10.1109/tmag.2022.3176061

c.     Zadehgol A, Lei H, Johnson B. A Methodology for Remote Sensing Inter-Turn Fault Events in Power System Air-Core Reactors, via Simulation of Magneto Quasi-Static Fields in 2D FDTD. IEEE Access. 2020; :1-1. DOI: 10.1109/access.2020.3024927

d.     Zadehgol A, Cangellaris A, Chapman P. A model for the quantitative electromagnetic analysis of an infinitely long solenoid with a laminated core. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields. 2011; 24(3):244-256.

Thanks to the following organizations for supporting our lab's research:

My former Alma Maters:

I'm an alumni of the following organizations: