Abstract
Today's mobile and interconnected society poses complex and growing challenges to the electronic systems that back emerging big data and Internet of Things technologies. As these systems execute increasingly complex algorithms and programs on vast datasets, ensuring their signal and power integrity (SPI) is essential to safeguarding their functionality and interoperability. The hardware systems that enable these advanced technologies and applications must be wireless, low-power/energy, high-speed, high-bandwidth, ultra-thin/small, scalable, reliable, and inexpensive. The design, optimization, fabrication, and testing of such hardware systems poses a formidable challenge spanning a variety of interdisciplinary fields while requiring the simultaneous modeling of coupled electrical, mechanical, and thermal responses of both passive interconnects and active devices. To further complicate matters, multi-physics models of these devices must be capable of quantifying uncertainty due to random variations in design, fabrication, and test processes, as well as (un)intentional noise in the environment. Ensuring the SPI of such electronic systems often requires high-fidelity (e.g., less than 10−6 field error) and full-wave quantification of the electromagnetic (information) waves. Modeling field propagation in such systems is nontrivial. Indeed, electromagnetic waves in these systems often propagate through multi-port networks of lossy, dispersive, inhomogeneous, multilayer (nonuniform), nonlinear, and multi-scale structures. The smallest features of these structures can range from nanometers on chip, to micrometers on package, to millimeters on system. In addition, the flow of signal and power across the complex spatial and temporal dimensions is influenced by thermal and power management specifications. Analytical methods are useful for modeling electromagnetic fields on simple and symmetric structures. While they oftentimes provide useful insights relating to the system response, used by themselves, they have limited range and scope. To solve problems of practical interest, computational electromagnetic (CEM) methods are indispensable. Although recent advancements in CEM methods have enabled solutions to problems considered nearly impossible only a decade ago, many practical applications remain out of the range of present-day algorithms. As a result, CEM practitioners are often forced to trade accuracy for computational cost (memory and time) while accepting an error that depends on the specific problem, the chosen CEM method, and the simplifying assumptions. This Special Issue provides a glimpse into new CEM trends for SPI problems. A Special Issue on this topic cannot be comprehensive; therefore, the intent is to motivate the reader to further explore the vast SPI literature, and to stimulate the development of novel and exciting research, publications, and tools that define the state-of-the-art in the field. Finally, the hope is to inform and generate awareness in the wider engineering community about the significant interdisciplinary challenges and the proportionally significant opportunities in this important and promising field. I'd like to extend my sincere gratitude to all authors for their high-quality contributions. I also would like to thank all reviewers for their invaluable time and efforts to improve the quality of the papers.
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