Addressing MIPI M-PHY connectivity challenges for more efficient testing
Chris Loberg, Tektronix
embedded.com (September 20, 2014)
As the industry moves to adopt the MIPI Alliance's M-PHY standard, designers are encountering some significant challenges related to oscilloscope measurements and, more specifically, probing. These challenges include strict requirements such as bus termination and input return loss, as well as the need to minimize common mode loading on the device under test (DUT) and signal fidelity requirements such as wide bandwidth, low noise, and high sensitivity.
The intent of this article is to provide information that will increase your chances of accurate and repeatable test results to ensure compliance with the standard. We will first review the requirements of the M-PHY standard relevant to oscilloscope probing, discuss the tests required in the M-PHY Physical Layer Conformance Test Suite (CTS), and provide practical examples of M-PHY probing with currently available oscilloscopes and probes.
Related Semiconductor IP
- MIPI MPHY v3.1, 1Tx-1Rx Type-1, SMIC 40LL, N/S orientation
- MIPI MPHY v3.1, 2Tx-2Rx Type-1, UMC 22ULL 1.8V, N/S orientation
- MIPI MPHY v3.1, 1Tx-1Rx Type-1, TSMC 55LP,
- MIPI MPHY v3.1, 2Tx-2Rx Type-1, TSMC 28HPC+, N/S orientation
- MIPI MPHY v3.1, 2Tx-2Rx Type-1, TSMC 16FFC, N/S orientation(ASIL-B)
Related White Papers
- Understand and perform testing for MIPI M-PHY compliance
- Integrating VESA DSC and MIPI DSI in a System-on-Chip (SoC): Addressing Design Challenges and Leveraging Arasan IP Portfolio
- Shift Left for More Efficient Block Design and Chip Integration
- Making SPI-4.2 Implementations More Efficient: Part 2
Latest White Papers
- New Realities Demand a New Approach to System Verification and Validation
- How silicon and circuit optimizations help FPGAs offer lower size, power and cost in video bridging applications
- Sustainable Hardware Specialization
- PCIe IP With Enhanced Security For The Automotive Market
- Top 5 Reasons why CPU is the Best Processor for AI Inference