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.