摘要:
Model optimization approaches based on spectral sensitivity is described. For example, a method includes determining a first model of a structure. The first model is based on a first set of parameters. A set of spectral sensitivity variations data is determined for the structure. Spectral sensitivity is determined by derivatives of the spectra with respect to the first set of parameters. The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data. The second model of the structure is based on a second set of parameters different from the first set of parameters. A simulated spectrum derived from the second model of the structure is then provided.
摘要:
Model optimization approaches based on spectral sensitivity is described. For example, a method includes determining a first model of a structure. The first model is based on a first set of parameters. A set of spectral sensitivity variations data is determined for the structure. Spectral sensitivity is determined by derivatives of the spectra with respect to the first set of parameters. The first model of the structure is modified to provide a second model of the structure based on the set of spectral sensitivity variations data. The second model of the structure is based on a second set of parameters different from the first set of parameters. A simulated spectrum derived from the second model of the structure is then provided.
摘要:
Dynamic removal of correlation of highly-correlated parameters for optical metrology is described. An embodiment of a method includes determining a model of a structure, the model including a set of parameters; performing optical metrology measurement of the structure, including collecting spectra data on a hardware element; during the measurement of the structure, dynamically removing correlation of two or more parameters of the set of parameters, an iteration of the dynamic removal of correlation including: generating a Jacobian matrix of the set of parameters, applying a singular value decomposition of the Jacobian matrix, selecting a subset of the set of parameters, and computing a direction of the parameter search based on the subset of parameters. If the model does not converge, performing one or more additional iterations of the dynamic removal of correlation until the model converges; and if the model does converge, reporting the results of the measurement.