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.
This application is a continuation of application Ser. No. 09/542,724, filed Apr. 4, 2000, now U.S. Pat. No. 6,532,076, entitled "METHOD AND APPARATUS FOR MULTIDOMAIN DATA ANALYSIS."
Provided is a method for determining a recovery schedule. The method includes accepting as input a recovery graph. The recovery graph presents one or more strategies for data recovery. In addition, at least one objective is provided and accepted. The recovery graph is formalized as an optimization problem for the provided objective. When formalized as an optimization problem, at least one solution technique is applied to determine at least one recovery schedule.
Measurement data sets for optical metrology systems can be processed in parallel using Multiple Tool and Structure Analysis (MTSA). In an MTSA procedure, at least one parameter that is common to the data sets can be coupled as a global parameter. Setting this parameter as global allows a regression on each data set to contain fewer fitting parameters, making the process is less complex, requiring less processing capacity, and providing more accurate results. MTSA can analyze multiple structures measured on a single tool, or a single structure measured on separate tools. For a multiple tool recipe, a minimized regression solution can be applied back to each tool to determine whether the recipe is optimized. If the recipe does not provide accurate results for each tool, search parameters and/or spaces can be modified in an iterative manner until an optimized solution is obtained that provides acceptable solutions on each tool.
