Probability Distribution Maps for Signal Integrity Noise Analysis
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Solution Overview
Problem
Current Signal Integrity analysis methods face challenges in accurately analyzing noise and crosstalk, particularly in producing convergent eye diagrams, compensating for measurement instrument noise, and visualizing crosstalk effects, due to limitations in existing tools like eye diagrams and contour plots which are non-convergent and ambiguous in separating noise and jitter contributions.
Innovation Solution
The approach involves categorizing unit intervals based on similar histories to create convergent probability distribution maps and cumulative distribution function maps, which allow for compensation of measurement noise and visualization of crosstalk effects, using parameterized distributions to generate superior eye diagrams and contour plots that extend beyond the central region, and isolating systematic and non-systematic waveforms to analyze jitter, noise, and crosstalk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional eye diagrams and contour plots are used for signal integrity analysis, then the analysis can be performed with existing tools, but the results are non-convergent and ambiguous in separating noise and jitter contributions
Solution Approach 1:
The patent segments the signal analysis by separating noise and jitter contributions into distinct components. It categorizes unit intervals based on similar histories and creates separate probability distribution maps for different noise and jitter sources, enabling precise separation and independent analysis of these intertwined phenomena
Solution Approach 2:
The patent introduces probability distribution maps and cumulative distribution function maps as intermediary representations between the raw signal and the final analysis results. These maps serve as mediators that systematically relate signal characteristics to noise and jitter contributions, providing a convergent and unambiguous analysis framework
2Measurement precision
If measurement instruments are used to observe data channels, then signal characteristics can be captured, but the instruments introduce their own noise contributions that contaminate the measurement
Solution Approach 1:
The patent extracts and isolates the measurement instrument noise from the total observed noise. By categorizing unit intervals and analyzing probability distributions, it separates the instrument-contributed noise from the intrinsic channel noise and crosstalk, allowing for independent characterization and compensation
Solution Approach 2:
The patent employs feedback mechanisms where the measured noise characteristics are used to adjust and compensate for instrument noise contributions. The probability distribution maps provide feedback information about the relative magnitudes of different noise sources, enabling iterative refinement of the noise separation and compensation process
3Productivity
If more serial data channels are packed into close proximity to increase productivity, then data transmission capacity improves, but crosstalk interference between channels increases
Solution Approach 1:
The patent applies local quality analysis by categorizing unit intervals based on their specific historical patterns and local signal characteristics. It identifies and characterizes crosstalk effects in specific local regions of the signal space, enabling targeted analysis and compensation for crosstalk from neighboring channels without affecting the entire signal
4Reliability
If noise analysis is performed to identify crosstalk sources, then signal integrity can be improved, but the analysis complexity increases due to multiple noise components
Solution Approach 1:
The patent transitions from traditional one-dimensional signal analysis to multi-dimensional probability distribution analysis. By introducing additional dimensions for noise categorization, histogram binning, and cumulative distribution functions, it systematically organizes complex multi-component noise into structured multi-dimensional representations that are easier to analyze and interpret
Data Source
AI summary
A method and apparatus for generating a probability density function eye are provided. The method preferably includes the steps of acquiring an input waveform, performing a clock data recovery in accordance with the input waveform to determine one or more expected transition times and defining a plurality of unit intervals of the input waveform in accordance with the one or more expected transition times. One or more values of one or more data points may then be determined in accordance with the input waveform in accordance with the one or more expected transition times, and a category for each unit interval in accordance with its state and its position within the input waveform may also be determined. One or more histograms may then be generated for the determined one or more values for each category of unit intervals.