| Tartalmi leírás: |
During worst-case circuit analysis (WCA), the primary task is to identify extreme values, and it is crucial to perform WCA efficiently. In the case of circuit analysis tasks, the size and complexity (number of parameters) of circuit designs are increasing, which increases the time required for simulations or calculations. As a possible solution for complexity reduction, we set the efficient separation/decomposition of circuit models as our research goal.
The decomposition reduces the WCA task to low-dimensional subproblems, making the task less computationally demanding. Decomposition aims to group the parameters into subsets such that the parameters in disjoint subsets do not influence each other’s effect on the output.
In this study, we investigate the potential advantages of model decomposition. As a possible method for decomposition, the combination of Monte Carlo simulation and sensitivity analysis is used.
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