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An optical measuring device generates a plurality of measured optical data from inspection of a thin film stack. The measured optical data group naturally into several domains. In turn the thin film parameters associated with the data fall into two categories: local and global. Local “genes&#

An optical measuring device generates a plurality of measured optical data from inspection of a thin film stack. The measured optical data group naturally into several domains. In turn the thin film parameters associated with the data fall into two categories: local and global. Local “genes” represent parameters that are associated with only one domain, while global genes represent parameters that are associated with multiple domains. A processor evolves models for the data associated with each domain, which models are compared to the measured data, and a “best fit” solution is provided as the result. Each model of theoretical data is represented by an underlying “genotype” which is an ordered set of the genes. For each domain a “population” of genotypes is evolved through the use of a genetic algorithm. The global genes are allowed to “migrate” among multiple domains during the evolution process. Each genotype has a fitness associated therewith based on how much the theoretical data predicted by the genotype differs from the measured data. During the evolution process, individual genotypes are selected based on fitness, then a genetic operation is performed on the selected genotypes to produce new genotypes. Multiple generations of genotypes are evolved until an acceptable solution is obtained or other termination criterion is satisfied.

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1. A multidomain process for evaluating parameters of a semiconductor wafer or wafer set comprising:generating measured data for multiple points on the semiconductor wafer or wafer set;defining multiple domains, wherein a domain has corresponding measured data;applying an iterative search method to

1. A multidomain process for evaluating parameters of a semiconductor wafer or wafer set comprising:generating measured data for multiple points on the semiconductor wafer or wafer set;defining multiple domains, wherein a domain has corresponding measured data;applying an iterative search method to each domain in order to generate a group of optimized semiconductor wafer parameter values for each domain, said iterative search method including:generating a plurality of sets of theoretical semiconductor wafer parameter values, and deriving a set of theoretical measurement data for each set of theoretical semiconductor wafer parameter values;associating different sets of theoretical semiconductor wafer parameter values with each domain;comparing a first set of theoretical measurement data, derived from a set of theoretical semiconductor wafer parameter values associated with a domain, to the measured data corresponding to the domain; anditeratively generating a new set of theoretical semiconductor wafer parameter values to be associated with the domain based on the comparing in a manner so as to associate increasingly more optimal theoretical semiconductor wafer parameter values with the domain, wherein the generation of the new set of theoretical semiconductor parameter values includes migrating at least one theoretical semiconductor parameter value from a set of theoretical semiconductor parameter values that has been associated with a different domain. 2. The method of claim 1, wherein the applying an iterative search method includes generating new theoretical semiconductor wafer parameter values in accordance with a genetic algorithm. 3. The method of claim 1, wherein said generating a plurality of sets of theoretical wafer parameter values includes defining a plurality of genotypes wherein a genotype includes a plurality of genes, wherein the plurality of genes correspond to different wafer parameters. 4. The method of claim 3, wherein one or more of the plurality of genes is defined as belonging to a first class where genes of the first class are optimized for each domain, and one or more of the plurality of genes is defined as belonging to a second class where genes of the second class are optimized across a plurality of domains, and one or more of the plurality genes is defined as belonging to a third class where genes of the third class are optimized across all domains, such that the genes of the third class are deemed to have the same value for each domain. 5. The method of claim 3, wherein each of the genes of the plurality of genes is defined as belonging to one class of a group of different gene classes, wherein the group of different gene classes includes a first class where genes of the first class are optimized for each domain, a second class where genes of the second class are optimized across a plurality of domains, and a third class where genes of the third class are optimized across all domains, such that the genes of the third class are deemed to have the same value for each domain. 6. A multidomain process for evaluating parameters of a semiconductor wafer or wafer set comprising:identifying a group of semiconductor wafer parameters to be evaluated;mapping the semiconductor wafer parameters into at least one genotype, said genotype comprising a collection of genes, each gene corresponding to a selected one of the group of semiconductor wafer parameters to be evaluated, wherein each gene belongs to one of a plurality of gene classes;defining more than one domain as a collection of genotypes, each domain having its own population of genotypes;comparing a set of measurement data to a corresponding set of theoretical data derived for each genotype in each domain in order to determine a level of fitness for each genotype;iteratively modifying a genotype population of a domain so that a correspondence between measurement data and the theoretical data derived for at least one genotype of the population of genotype s becomes more fit; andwherein the plurality of gene classes include a first type of gene class where genes in the first class are optimized for each domain, and the plurality of gene classes including a second type of gene class where genes in the second type of class are optimized across a plurality of domains, and the plurality of gene classes including a third type of gene class where genes in the third type of class are optimized across all domains. 7. The method of claim 6, further including migrating genes among the domains by selecting at least one gene from a genotype of a current domain population of genotypes and using the selected gene to replace a corresponding gene in a genotype of another domain population of genotypes such that the selected gene is incorporated into a genotype of the genotype population of a different domain. 8. The method of claim 6, further including evolving a next population of genotypes for a domain by selecting at least one genotype from a current population based on a fitness level of the genotype and performing a genetic operation on the at least one genotype, thereby creating at least one new genotype and adding the at least one new genotype to the next population. 9. The method of claim 8, further including repeating the comparing, and evolving so that for each domain a fittest genotype becomes increasing more fit.