Entropy-Based Software Clock Recovery for High-Jitter RET Oscilloscopes
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Solution Overview
Problem
Real-equivalent-time (RET) oscilloscopes face challenges with slow processing speed and poor handling of signals with larger impairments, such as jitter, due to iterative methods like standard deviation-based unit interval estimation, which are not suitable for high-speed signaling applications.
Innovation Solution
Employing entropy-based methods to determine the most accurate unit interval using two-dimensional histograms, optionally supplemented by machine learning neural networks, to improve processing speed and accuracy in RET oscilloscopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative standard deviation-based unit interval estimation is used, then measurement precision is improved, but processing speed deteriorates
Solution Approach 1:
The patent replaces the iterative mechanical calculation process (standard deviation-based estimation) with an entropy-based computational approach. By using entropy calculation on histogram data, the system achieves accurate unit interval estimation without requiring multiple iterative steps, thus maintaining measurement precision while dramatically improving processing speed.
Solution Approach 2:
The patent changes the fundamental parameter used for unit interval estimation from standard deviation to entropy. This parameter transformation allows the system to evaluate signal quality and determine unit interval accuracy through a single entropy calculation rather than iterative standard deviation computations, resolving the speed-precision contradiction.
2Measurement precision
If iterative methods are used for unit interval estimation, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary histogram generation from the sampled signal, which contains all the necessary information for unit interval estimation. By pre-computing the histogram and then using entropy calculation on this prepared data, the system avoids time-consuming iterative processes during the actual estimation phase, reducing processing time while maintaining precision.
3Measurement precision
If regular SCDR methods are used, then clock recovery accuracy is improved, but adaptability to aliased signals deteriorates
Solution Approach 1:
The patent fundamentally changes the approach to clock recovery by using entropy-based unit interval estimation that is specifically adapted for aliased signals. Instead of using regular SCDR methods that assume non-aliased input, the entropy method works directly with the aliased histogram data, maintaining clock recovery accuracy while achieving adaptability to RET oscilloscope signal conditions.
Data Source
AI summary
An oscilloscope having a Nyquist frequency lower than an analog bandwidth includes an input configured to receive a signal under test; an analog-to-digital converter (ADC) to receive the signal under test, sample the signal under test at a sample rate, and produce digital samples of the signal under test; one or more processors configured to execute code that causes the one or more processors to: determine a set of candidate unit intervals by generating corresponding candidate histograms using the candidate unit intervals; determine a best unit interval from the candidate unit intervals based upon entropy measures of each candidate histogram; and reconstruct a representation of the signal under test using the digital samples and the best unit interval.