Measurement Circuit for Fast Frequency-Domain DUT Parameter Estimation
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Solution Overview
Problem
Existing techniques for estimating device under test (DUT) model parameters in the frequency domain, such as non-linear least squares algorithms, face challenges with convergence speed and accuracy under varying conditions like different ICs, temperatures, and DUT age.
Innovation Solution
A parameter convergence model is employed that adjusts a regularization parameter in iterations based on cost function improvement, using a cost function that accounts for error residuals, to converge to within a target tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-linear least squares algorithm is used to estimate DUT model parameters in frequency domain, then parameter estimation can be performed, but convergence is time-consuming and results are inaccurate for different conditions
Solution Approach 1:
The patent applies preliminary action by using an improved Tikhonov regularization method that pre-establishes a stable computational framework with properly selected regularization parameters. This preliminary setup avoids the iterative convergence issues of non-linear least squares, providing accurate parameter estimates without time-consuming iterations.
Solution Approach 2:
The patent employs parameter changes by transforming the ill-posed parameter estimation problem into a well-posed problem through regularization parameter selection. By changing the mathematical approach from direct non-linear least squares to regularized least squares with optimized parameters, both accuracy and convergence speed are improved.
2Adaptability or versatility
If non-linear least squares algorithm is used for parameter estimation, then estimation can be performed, but results are inaccurate under varying conditions such as different ICs, temperatures, and DUT age
Solution Approach 1:
The patent applies dynamics by developing a regularization parameter selection method that adapts to varying conditions (different ICs, temperatures, DUT age). The dynamic adjustment of regularization parameters ensures the estimation method remains accurate and reliable across all operating conditions and device states.
Solution Approach 2:
The patent uses parameter changes by modifying the regularization approach to account for varying conditions. By changing the mathematical model to include condition-dependent regularization parameters, the system achieves both adaptability to different conditions and maintained measurement precision.
3Reliability
If traditional parameter estimation methods are used, then estimation can be performed, but false local minima are encountered and estimation error linearity is lost
Solution Approach 1:
The patent applies feedback by implementing a regularization parameter selection mechanism that monitors and adjusts parameters based on error analysis. This feedback loop prevents convergence to false local minima and maintains estimation error linearity by continuously optimizing the regularization parameters based on actual estimation performance.
Data Source
AI summary
A circuit for determining device under test (DUT) model parameters is described. The circuit includes a parameter estimator circuit configured to: obtain initial values for DUT model parameters based on sense signal samples; execute a parameter convergence model having a regularization parameter and a cost function that accounts for error residuals; and obtain final values for the DUT model parameters by adjusting the regularization parameter in iterations of the parameter convergence model as a function of cost function improvement until the parameter convergence model converges to within a target tolerance.