Circuit Simulation for Automated Measurement Uncertainty Calculation
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Solution Overview
Problem
High-frequency test systems face uncertainties due to impedance mismatches, temperature stability, aging, noise, and instrument accuracy, requiring manual metrological calculations that are time-consuming and labor-intensive, especially when system configurations change.
Innovation Solution
A model of the test system is developed and simulated using a circuit simulator, varying uncertainty terms within a probability distribution to generate a statistical distribution of test results, allowing for automated calculation of measurement uncertainties without manual derivation of equations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual metrological calculations are used to calculate measurement uncertainties, then the calculation accuracy can be ensured, but the time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual metrological calculations with automated computer-based simulations. The measurement uncertainty calculation process is transformed from a manual analytical method to an automated computational method using Monte Carlo simulations and circuit simulators, eliminating the need for highly trained metrologists while maintaining calculation accuracy.
Solution Approach 2:
The patent creates virtual models (copies) of the physical test system including DUT, test instruments, and interconnecting components. These simulated representations allow uncertainty calculations to be performed on digital models rather than requiring physical measurement and manual computation, significantly reducing time while preserving accuracy.
2Measurement precision
If manual metrological analysis is performed to develop uncertainty calculations, then the uncertainty equations can be accurately derived, but the process requires highly trained metrologists and takes several days
Solution Approach 1:
The patent enables the test system to perform its own uncertainty analysis through automated simulations. The computer automatically runs Monte Carlo simulations, varies input parameters according to their probability distributions, and computes output uncertainties without requiring external metrological expertise, making the system self-analyzing.
Solution Approach 2:
The patent transforms the uncertainty calculation from solving complex analytical equations to performing parameter variations in simulations. By changing the approach from mathematical derivation to computational experimentation with varied parameters, the process becomes automated while maintaining accuracy.
3Adaptability or versatility
If the topography of the test system is changed, then the test system can be adapted to different configurations, but the uncertainty calculations must be re-validated and changed
Solution Approach 1:
The patent makes the uncertainty calculation system dynamic and adaptive to configuration changes. When the test system topology changes, the computer automatically updates the simulation model and re-runs the Monte Carlo analyses without requiring manual re-derivation of uncertainty equations, allowing rapid adaptation to new configurations.
Solution Approach 2:
The patent creates a universal automated uncertainty analysis framework that can handle multiple test system configurations through a single simulation-based approach. The same computer-implemented method works across different DUTs, test instruments, and interconnections, eliminating the need for configuration-specific manual calculations.
4Measurement precision
If comprehensive uncertainty analysis is performed on all system components, then the measurement uncertainty can be accurately characterized, but the calculation complexity increases
Solution Approach 1:
The patent divides the test system into discrete segments (DUT, test instruments, interconnecting components) that can be independently modeled and simulated. Each component's uncertainty contributions are analyzed separately through individual parameter variations, then combined in the overall Monte Carlo simulation to provide comprehensive uncertainty characterization.
Data Source
AI summary
A method of determining a measurement uncertainty of a test system uses a test system model having a plurality of uncertainty terms entered into a simulator. The test system model is run on the simulator a sufficient number of iterations while randomly varying each of a first portion of the plurality of uncertainty terms within probability distributions to produce a statistically significant number of results of a selected parameter. The results are evaluated to determine a measurement uncertainty of the selected parameter.