Jitter Decomposition Using Spectral Analysis and PDF
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Solution Overview
Problem
Conventional methods for decomposing jitter in high-frequency signals inaccurately separate periodic and aperiodic bounded jitter components from random jitter, leading to exaggerated estimates of total jitter, which hampers signal quality analysis and diagnosis.
Innovation Solution
A test and measurement instrument with a jitter decomposition module that uses spectral analysis and time-domain probability density to isolate and decompose jitter components, specifically separating correlated deterministic jitter from uncorrelated residual jitter, and further distinguishing bounded uncorrelated jitter, allowing for more precise calculation of total jitter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional methods are used to decompose jitter by separating deterministic from random jitter, then the analysis process is simplified, but periodic and aperiodic bounded jitter components are erroneously included in random jitter, leading to exaggerated total jitter estimates
Solution Approach 1:
The invention segments the jitter decomposition process into multiple stages: first separating deterministic jitter (including periodic and data-dependent components) from random jitter, then further dividing the random jitter into bounded uncorrelated jitter and unbounded correlated jitter components. This multi-level segmentation prevents mis-qualification of bounded components while maintaining analytical simplicity.
Solution Approach 2:
The invention extracts and removes periodic jitter components through spectral analysis before performing random jitter separation. By taking out the deterministic periodic components first, the subsequent random jitter measurement is no longer biased, allowing for accurate extraction of true random jitter without contamination from bounded periodic components.
2Measurement precision
If spectral analysis is performed to remove periodic jitter before random jitter separation, then measurement precision improves, but device complexity increases
Solution Approach 1:
The invention performs spectral analysis as a preliminary action before the main jitter separation process. By removing periodic jitter components in advance, the subsequent random jitter separation operates on cleaned data, improving measurement precision without requiring complex real-time processing during the main analysis phase.
Solution Approach 2:
The invention uses spectral analysis as an intermediary step that bridges the raw jitter measurement and the final random jitter separation. This intermediary process filters out periodic components, creating a simplified dataset that is easier to analyze while maintaining high precision in the final measurements.
3Adaptability or versatility
If Q-scale plotting is used to infer random jitter sigma, then the analysis method is standardized, but presence of periodic jitter in the distribution biases the RJ sigma inference, reducing total jitter calculation accuracy
Solution Approach 1:
The invention performs spectral analysis as a preliminary action to remove periodic jitter components before applying Q-scale plotting. This ensures that the standardized Q-scale analysis operates on data free from periodic contamination, allowing accurate inference of random jitter sigma while maintaining method standardization.
Solution Approach 2:
The invention extracts and removes periodic jitter components through spectral analysis before the Q-scale plotting step. By taking out these biased components, the subsequent standardized Q-scale analysis yields accurate random jitter measurements without the distortion that would otherwise occur from periodic jitter presence.
Data Source
AI summary
A method for analyzing jitter using a test and measurement instrument includes obtaining a collection of time interval error (TIE) values corresponding to composite jitter of a waveform, optionally decomposing the composite jitter into jitter components that are correlated to the data pattern and components that are uncorrelated to the data pattern, and using a spectral approach to decompose the jitter components into jitter components that are recognizable as deterministic and jitter components that are unrecognizable as deterministic. Thereafter, the jitter components analyzed in the frequency domain are converted back to the time domain, and subtracted from the composite jitter, thereby isolating uncorrelated residual jitter. The uncorrelated residual jitter is decomposed into bounded uncorrelated jitter and random jitter, for example, by integrating a probability density (PDF) function of the residual jitter and analyzing the resulting cumulative distribution function (CDF) curve in Q-space.