Synchronizing sample clocks of a data converter array
Kazim Peker and Altug Oz, Analog Devices, Inc.
EDN (June 29, 2016)
The requirements of higher system bandwidth and resolution in a variety of applications from communications infrastructure to instrumentation drive up the demand for connecting multiple data converters in an array form. Designers must find low noise and high accuracy solutions to clock and synchronize a large array of data converters using the common JESD204B serial data converter interface.
Clock generation devices containing jitter attenuation functions, internal VCOs, and a multitude of outputs and many synchronization management functions, are now coming to market to address this system problem. In many real-life applications, however, the sheer number of required clocks in a data converter array exceeds what may be feasible to obtain from a single IC component. Designers often resort to connecting multiple clock generation and clock distribution components together, thus, creating a broad “clock tree”.
This article provides a real-life case study of how to build a flexible and re-programmable clock expansion network, that maintains not only an excellent phase noise/jitter performance, but also passes-on the required synchronization information from the 1st device of the clock tree to the last one with deterministic control.
Related Semiconductor IP
- JESD204E Controller IP
- eUSB2V2.0 Controller + PHY IP
- I/O Library with LVDS in SkyWater 90nm
- 50G PON LDPC Encoder/Decoder
- UALink Controller
Related Articles
- LVDS ups A/D converter data rates
- A comparison of SRAM vs quantum-derived semiconductor PUFs
- Paving the way for the next generation of audio codec for True Wireless Stereo (TWS) applications - PART 5 : Cutting time to market in a safe and timely manner
- The Network Switch: Unsung Hero of the Hyper-Scale Data Center
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