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Previous: 5.1.1 Inverse Modeling Up: 5.1 Introduction Next: 5.2 Optimization Framework |
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Previous: 5.1.1 Inverse Modeling Up: 5.1 Introduction Next: 5.2 Optimization Framework |
Although models used in TCAD simulators are very sophisticated, there remain uncertainties in some of the parameters of a model. This is due to the abstraction that takes place whenever a physical effect is approximated by a mathematical model. Such models usually contain parameters that serve as physical constants but the exact values are either not very well known or not measurable respectively. Other parameters (fitting parameters) might not even be physically motivated but serve as a help to best characterize a certain effect. Calibration is used to fit such parameters of a simulator model to measured data. An uncalibrated simulator can not be expected to deliver accurate results.
From the point of view of the optimization this task is quite similar to the inverse modeling task as described above. The only difference is that there is no need for a parameterizable Wafer model. Instead, a Wafer that best characterizes the process family should be used.
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Previous: 5.1.1 Inverse Modeling Up: 5.1 Introduction Next: 5.2 Optimization Framework |