Meet the SERDES challenge: Design a high-speed serial backplane
Michael Heimlich
EETimes (10/6/2011 9:47 AM EDT)
The benefit of having high-frequency design tools resident on a vector network analyzer (VNA) does not become obvious until the time comes to compare simulation to measurements. At this point, the advantage of a more streamlined work flow -- without the impediment of transferring data to a simulator running on a separate PC or workstation - becomes clear. To illustrate the benefit of such an
approach, this article follows the design flow for a high-speed serial backplane.
BACKGROUND: THE SERDES CHALLENGE
Increasing chip-to-chip, board-to-board, and system-to-system communications data rates have created the need for multi-gigabit asynchronous signaling schemes in which serializer/deserializer (SERDES) technology is used to format and transfer data. Analysts predict that SERDES I/O data rates will double every two to three years, so speeds in excess of 8Gb/s are already on the way and placing the SERDES design challenge clearly into the microwave domain. Consequently, designers familiar only with lower-speed buses will now be facing new physical and electrical design challenges.
To read the full article, click here
Related Semiconductor IP
- SerDes
- Automotive Grade PLLs, Oscillators, SerDes PMAs, LVDS/CML IP
- 100G SerDes PAM4 PHY
- 32Gbps SerDes PHY in GF 22nm
- Ultra-Low Latency 32Gbps SerDes IP in TSMC 12nm FFC
Related Articles
- Analyzing High-Speed Serial Links (Rambus)
- High-Speed Serial fully digital interface between WLAN RF and BB chips
- Multi-Gigabit SerDes: The Cornerstone of High Speed Serial Interconnects
- A signal conditioner for high-speed serial links
Latest Articles
- Crypto-RV: High-Efficiency FPGA-Based RISC-V Cryptographic Co-Processor for IoT Security
- In-Pipeline Integration of Digital In-Memory-Computing into RISC-V Vector Architecture to Accelerate Deep Learning
- QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design
- ChipBench: A Next-Step Benchmark for Evaluating LLM Performance in AI-Aided Chip Design
- COVERT: Trojan Detection in COTS Hardware via Statistical Activation of Microarchitectural Events