Vector Optimization for Electronic Circuit Parameter Determination
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Solution Overview
Problem
Determining the variation of physical properties in electronic circuits and microelectromechanical systems is challenging due to the complexity of physical models and the need for numerous experiments, especially when dealing with a large number of physical parameters, which is time- and cost-prohibitive.
Innovation Solution
A method to determine specific values of physical parameters by iteratively modifying vectors to maximize the smallest average distance between projected sub-spaces, allowing for the optimization of experiment points without prior knowledge of the behavioral model, using a greedy algorithm and reliability indexes to model physical properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical models are used to determine the variation of physical properties, then the understanding of system behavior is improved, but the complexity of the models and the number of parameters make simulations impossible over all variation ranges
Solution Approach 1:
The patent segments the parameter space into discrete grids along each physical parameter dimension. This segmentation allows the complex continuous optimization problem to be broken down into manageable discrete steps, where experiments are systematically placed at grid points rather than requiring continuous simulation across all parameter variations.
Solution Approach 2:
The patent introduces a new dimension by projecting parameter vectors onto sub-spaces of dimension k (where k < p). This dimensional reduction allows the system to evaluate and optimize experimental designs in lower-dimensional projections, making the problem computationally tractable while still capturing the essential behavior across all p physical parameters.
2Productivity
If the number of experiments is decreased to reduce time and cost, then the resource consumption is reduced, but the number of experiments becomes insufficient to determine the full evolution of the physical property
Solution Approach 1:
The patent employs an iterative dynamic optimization process where the experimental design is progressively refined. Starting from an initial set of experiments, the algorithm iteratively modifies the design by evaluating optimality criteria and making adjustments, allowing the experiment set to adapt and improve coverage of the parameter space dynamically rather than using a static fixed number of experiments.
Solution Approach 2:
The patent replaces the traditional approach of conducting numerous physical experiments with a computational optimization system. Instead of mechanically performing many tests, the system uses algorithms to evaluate optimality criteria, calculate distances between parameter vectors, and determine the most informative experiment points, substituting computational intelligence for brute-force experimental exploration.
3Reliability
If systematic techniques for determining optimal experimental design are used, then the quality of the behavioral model is improved, but the number of experiments required becomes too large when the number of physical parameters is large
Solution Approach 1:
The patent changes the approach by introducing an optimality criterion that evaluates experimental designs based on the distances between parameter vectors and their projections onto sub-spaces. This parameter transformation allows the system to identify a smaller subset of experiments that maximally inform the behavioral model, replacing the need for large numbers of experiments with a strategically optimized minimal set.
Data Source
AI summary
A method for determining, for each of at least p physical parameters of one or several components of an electronic circuit or of a microelectromechanical system, a number n of experiment values of the physical parameter includes determining n vectors of dimension p, each component of each of the vectors corresponding to one of n initial values of one of physical parameters; and iteratively modifying at least some of the n vectors to bring to a maximum, at least locally, for each pair of vectors from among pairs of n vectors, the smallest average of the sum of distances between the vectors of said pair projected onto sub-spaces of dimension k, where k belongs to a set of integers ranging between 1 and p and at least comprising 1, 2, and p, the components of each of the n vectors corresponding, at the end of the iterations, to experiment values.