Stephen Scearce
Senior Director, Cable Backplane Systems / Amphenol Communications Solutions
Title:
High-Speed Cable Backplane Systems for AI Infrastructure: Speed of light Challenges at
224G and 448G
Biography:
Stephen Scearce is Senior Director of Cable Backplane Systems at Amphenol Communications
Solutions, leading a global engineering organization responsible for the AI industry's
high-speed cable backplane interconnects at 224G and 448G data rates. With over 25 years
of experience in signal integrity, power integrity, mechanical/thermal design, and
high-speed hardware systems, his career spans NASA, Cisco Systems, and now Amphenol. He
holds a B.S. in Engineering Technology and an M.S. in Electrical Engineering, both Summa
Cum Laude, from Old Dominion University, and holds multiple U.S. patents in signal and
power integrity, EMC and Mechanical design. He is a longtime volunteer with the IEEE EMC
Society, currently serving as Finance Vice President and previously as co-chair of the
IEEE EMC+SIPI 2025 Symposium in Raleigh, NC.
Abstract:
As AI training and inference clusters scale to unprecedented bandwidth requirements,
cable backplane interconnects have become a critical bottleneck and enabler for
next-generation system architectures. This presentation examines the engineering
challenges of designing, qualifying, and deploying high-speed cable backplane systems at
224 Gb/s and emerging 448 Gb/s data rates for AI infrastructure. Topics include signal
integrity considerations for copper twinax cable at extreme data rates (including
PAM4/PAM6 modulation schemes), mechanical float and connector design for reliable
high-density interconnects, system BERT-based qualification methodologies, and lessons
learned from large-scale cartridge reliability programs. The talk will address the
tradeoffs between insertion loss, reach, and manufacturability that engineering teams
must navigate as the industry pushes toward higher bandwidth per lane, and will share
practical approaches to qualification and reliability testing that support high-volume
deployment in AI data center environments.