PCB Component Model Extraction via Stochastic Optimization
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Solution Overview
Problem
Existing Electronic Design Automation (EDA) tools face challenges in accurately modeling components of serial communications links, particularly for components like vias and other devices, due to difficulties in measuring their characteristics using vector network analyzers, as these components are not readily accessible for direct testing.
Innovation Solution
The method involves using stochastic optimization to determine the s-parameters of components in PCB channels by analyzing measurements from two symmetrical channels with different lengths, allowing for the construction of accurate component models that can be used in EDA tools based on physical measurement data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vector network analyzer probes are used to measure component characteristics directly, then measurement capability is provided, but the components (vias, devices) cannot be physically connected to probes due to accessibility issues
Solution Approach 1:
The patent introduces PCB channels with known characteristics as intermediary structures. Instead of directly measuring inaccessible components, the system measures the entire channel (which includes the component) and uses the known characteristics of the channel to mathematically extract the component's s-parameters. This intermediary approach bridges the gap between accessible measurement points and inaccessible components.
Solution Approach 2:
The patent creates virtual copies of component characteristics through mathematical modeling. By measuring the complete channel and comparing it against known channel characteristics, the system generates accurate s-parameter models of individual components without physically interacting with them. These virtual models replicate the component's electrical behavior for EDA tool analysis.
2Measurement precision
If complete channel models are generated using VNA measurements, then accurate channel-level modeling is achieved, but individual component models cannot be extracted
Solution Approach 1:
The patent segments the complete channel measurement data into individual component characteristics. By measuring the entire channel and using the known characteristics of the channel structure, the system mathematically separates and extracts the s-parameters of individual components (vias, traces, devices) from the overall channel measurement, preserving component-level detail information.
Solution Approach 2:
The patent extracts component s-parameters from the complete channel measurement data. Using the known channel characteristics as a reference, the system isolates and removes the contribution of individual components from the overall channel response, thereby obtaining accurate models of each component separately while maintaining the accuracy of the complete channel model.
3Measurement precision
If stochastic optimization is applied to search multi-dimensional parameter space, then accurate component s-parameters are determined, but computational complexity increases
Solution Approach 1:
The patent implements feedback mechanisms in the stochastic optimization process. The algorithm iteratively adjusts component parameter guesses, comparing predicted channel responses against actual measurements, and uses this feedback to converge on accurate s-parameter values. This feedback loop enables precise extraction despite the complexity of the multi-dimensional parameter search space.
Data Source
AI summary
Various embodiments herein include one or more of systems, methods, software, and/or data structures to extract models of components (e.g., vias and traces) for PCB channels from measurements (or simulations) taken from physical PCB channels. By applying stochastic optimization to measurements of two PCB channels having different channel lengths, s-matrices (e.g., two-port, four-port, and the like) of the components of a PCB channel may be accurately determined by searching the multi-dimensional parameter space for parameters that comply with the measured values. Once the models for the components have been accurately determined, they may be utilized in constructing a model library that includes component models and is based on physical measurement data.