Therma-wave, Inc.

Fujitsu Corporation

John J Sidorowich

Jawahar Jain

Stergios Stergiou

David Loren Marvit

B Thomas Adler

Alex Gilman

Yannis Labrou

Jon Opsal

Albert Reinhardt

In one embodiment, modeling topics includes accessing a corpus comprising documents that include words. Words of a document are selected as keywords of the document. The documents are clustered according to the keywords to yield clusters, where each cluster corresponds to a topic. A statistical distribution is generated for a cluster from words of the documents of the cluster. A topic is modeled using the statistical distribution generated for the cluster corresponding to the topic.

Modeling topics using statistical distributions

In one embodiment, a set of target search terms for a search is received. Candidate terms are selected, where a candidate term is selected to reduce an ontology space of the search. The candidate terms are sent to a computer to recommend the candidate terms as search terms. In another embodiment, a document stored in one or more tangible media is accessed. A set of target tags for the document is received. Terms are selected, where a term is selected to reduce an ontology space of the document. The terms are sent to a computer to recommend the terms as tags.

Recommending terms to specify ontology space

In one embodiment, assigning tags to a document includes accessing the document, where the document comprises text units that include words. The following is performed for each text unit: a subset of words of a text unit is selected as candidate tags, relatedness is established among the candidate tags, and certain candidate tags are selected according to the established relatedness to yield a candidate tag set for the text unit. Relatedness between the candidate tags of each candidate tag set and the candidate tags of other candidate tag sets is determined. At least one candidate tag is assigned to the document according to the determined relatedness.

Selecting tags for a document by analyzing paragraphs of the document

In one embodiment, identifying clusters of words includes accessing a record that records affinities. An affinity between a first and second word describes a quantitative relationship between the first and second word. Clusters of words are identified according to the affinities. A cluster comprises words that are sufficiently affine with each other. A first word is sufficiently affine with a second word if the affinity between the first and second word satisfies one or more affinity criteria. A clustering analysis is performed using the clusters.

Identifying clusters of words according to word affinities

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 &#x201c;genotype&#x201d; which is an ordered set of the genes. For each domain a &#x201c;population&#x201d; of genotypes is evolved through the use of a genetic algorithm. The global genes are allowed to &#x201c;migrate&#x201d; 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.

Method and apparatus for multidomain data analysis

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 &ldquo;genes&rdquo; 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 &ldquo;best fit&rdquo; solution is provided as the result. Each model of theoretical data is represented by an underlying &ldquo;genotype&rdquo; which is an ordered set of the genes. For each domain a &ldquo;population&rdquo; of genotypes is evolved through the use of a genetic algorithm. The global genes are allowed to &ldquo;migrate&rdquo; 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.

An optical inspection device generates a plurality of measured optical data from inspection of a thin film stack. A processor evolves models of theoretical data, which 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 list of 'genes.' Each gene corresponds to a selected thin film parameter of interest. Many such individual genotypes are created thereby forming a 'population' of genotypes, which are evolved through the use of a genetic algorithm. Each genotype has a fitness associated therewith based on how much the theoretical data derived therefrom differs from the measured data. 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.

Growing community of inventors

Santa Cruz, CA, United States of America

John J Sidorowich