Simulation Model Parameter Tuning via Response Surface Error Mapping
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Solution Overview
Problem
Existing simulation models based on simplified physical models struggle to accurately reproduce the behavior of devices due to the difficulty in tuning numerous physical parameters, leading to inaccuracies in predicting device behavior.
Innovation Solution
A simulation model construction method that includes input data preparation, dataset generation, response surface creation, feature point identification, dataset updating, and model optimization to adjust physical parameters, combining physical and statistical models to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a physical model with many physical parameters is used to accurately reproduce device behavior, then model accuracy improves, but parameter tuning complexity and computational burden increase
Solution Approach 1:
The patent transforms the complex parameter tuning problem into an optimization problem by automatically adjusting physical parameters using objective functions and optimization algorithms. The system evaluates multiple parameter combinations and selects the optimal set that minimizes the difference between simulated and measured device behavior, thereby achieving high model accuracy without manual tuning complexity.
Solution Approach 2:
The patent creates a virtual copy of the device through simulation modeling, where the physical device's behavior is replicated in a computational environment. By optimizing the simulation model to match measured data, the system achieves accurate device behavior reproduction without directly manipulating the physical device, reducing experimental complexity.
2Ease of manufacture
If a simplified physical model is used, then ease of construction improves, but prediction accuracy deteriorates
Solution Approach 1:
The patent enhances simplified physical models by systematically optimizing their parameters against measured data. Instead of using complex models from the start, the system takes simpler models and improves their accuracy through automated parameter adjustment, achieving a balance between construction ease and prediction accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the simulation model's output is continuously compared with measured device behavior. The difference (error) between simulation and measurement feeds back into the parameter optimization process, iteratively improving the simplified model's accuracy until it adequately reproduces device characteristics.
Data Source
AI summary
A simulation model construction method, for constructing a simulation model which simulates input-output characteristics of a device and includes a physical model of the device, includes: preparing a plurality of pieces of input data to be input into the simulation model; generating a dataset including the plurality of pieces of input data and an output error relative to a measured value of an output value of the simulation model when each of the plurality of pieces of input data is input into the simulation model; generating a response surface of the output error for the input data, on the basis of the dataset; identifying a feature point where the output error is smallest on the response surface; generating an updated dataset by adding the feature point to the dataset; and optimizing a physical parameter included in the physical model using the updated dataset.