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Look Before You Leap

In this installation of Engineering Nightmares, Robert Haller explores a keystone rule to keep in mind when troubleshooting signal integrity problems. Learn why signal integrity engineers should never perform a measurement or simulation without first anticipating what they expect to see.



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A Three-Step Process for Characterizing Self-Generated Interference for Wireless or IoT Products

The proliferation of electronic products has made compatibility between devices increasingly important. Products must not interfere with one another and must be designed to be immune to external energy sources. Kenneth Wyatt helps product designers or EMC engineers learn how to characterize this self-generated EMI so that these issues are addressed early when costs and design changes are minimized.


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Noise in Traffic: Signal Emulation for Automotive Apps

DesignCon 2023 Best Paper Award Winner

Automotive applications present new challenges to high-speed serial technology. Asymmetric, multi-gigabit signaling between sensors, processors, and displays in the unique noise environments of both electric and internal combustion engine vehicles create new problems for signal and power integrity engineers. This paper introduces the signal impairments required for receiver testing in the emerging automotive standards like ASA, MIPI's A-PHY, Automotive Ethernet, and more. Standards specify different sources of noise in different ways, some in the form of time evolutions, others as spectra. This paper focuses on techniques for generating and calibrating each noise source while describing advanced de-embedding techniques and addressing test equipment limitations.


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Finite Element Modeling of Copper Foil Loss From AFM Measurements

DesignCon 2023 Best Paper Award Winner

The roughness of copper foils has a detrimental effect on signal loss in PCBs. Therefore, reducing roughness is crucial in minimizing signal loss. Nevertheless, roughness is essential to ensure a good adherence between the prepreg and the copper foil. A compromise must be found between adherence and power integrity. This paper presents a novel approach to evaluate signal loss without assuming any specific roughness shape.    


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Data-Efficient Supervised Machine Learning Technique for Practical PCB Noise Decoupling

DesignCon 2023 Best Paper Award Winner

Design of PCB-based PDNs has become a challenge due to rising power consumption, lowering supply voltages, increasing integration density and design complexity. In this paper, we propose an algorithmic procedure using supervised machine learning techniques to provide expert guidance on the PDN design and optimize power supply decoupling capacitors. The proposed method replaces the computationally expensive numerical simulations with faster ANNs.



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