Signal Integrity

Data-Efficient Supervised Machine Learning.jpg

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.



Read More
Figure 2 Barrie et al.jpg

Statistical BER Analysis of Concatenated FEC in Multi-Part Links

DesignCon 2023 Best Paper Award Winner

This paper proposes a model that can serve as a tool for evaluating FEC choices in 200+ Gb/s applications. It allows the comparison of the effect of different inner/outer codes and inner-FEC interleaving schemes on post-FEC BER. It can also be used as a tool for system-level transceiver design, allowing designers to see the impact of design choices on the post-FEC BER efficiently.


Read More
Keshav Amla Article Part 2 Cover.jpg

The Influence of Material Characteristics on High Speed Design Part 2: Roughness and Skew

Copper foil and oxide roughness are also major drivers of insertion loss behavior. As discussed in Part 1 of this article series, the first crucial step to efficacious design and development is understanding how the dielectric material attributes are to be carefully considered when assessing impedance and insertion loss performance. This article by Keshav Amla highlights the importance of considering roughness attributes in this assessment, ensuring a well-rounded approach to addressing impedance and loss, as well as skew, a signal integrity concern of ever-increasing importance.


Read More