Tokyo Electron Limited

Lyra Health, Inc.

Aaron Archer Waterman

Sanjeev Kaushal

Sukesh Janubhai Patel

Yuichiro Morozumi

Wolfgang Polak

Tetsushi Ozaki

Orion Wolfe

Daniella J Perlroth

Jessie M Burger

Armando Franco

A health provider matching service is provided to match patients to health providers based on a semantic relationship graph of data associated with conditions of patients. Using natural language processing, the service identifies terms describing symptoms, treatments, and health providers associated with a condition. The service then identifies semantic relations among the terms and probability distributions of the terms to generate a semantic relationship graph. Quality information of therapists is combined with machine learning techniques to identify features that are used to distinguish high quality and low quality providers. Based on the semantic relationship graph and the derived quality information, the service matches a patient to a health provider suitable to treat the particular needs of the patient. The service also handles social, behavioral, or emotional issues for which medications are not appropriate.

Health provider matching service

An optimization system optimizes groups of providers for a given population of users. The optimization system includes various engines such as a source interface engine, selection engine, and group score engine. The source interface engine receives information to be used in constrained optimization from server computers and stores the information in databases. The selection engine selects groups of providers from a set of all available providers. The group score engine generates a score for each group of providers. The selection engine continues to iteratively select groups of providers to minimize the generated score. The selection engine can use hard constraints, for example, requiring that a certain type of provider be included in all selected groups, or soft constraints, for example, requiring that a certain number of providers be included based on a size of the given population.

Constrained optimization for provider groups

A system and method autonomously determines the impact of respective tool parameters on tool performance in a semiconductor manufacturing system. A parameter impact identification system receives tool parameter and tool performance data for one or more process runs of the semiconductor fabrication system and generates a separate function for each tool parameter characterizing the behavior of a tool performance indicator in terms of a single one of the tool parameters. Each function is then scored according to how well the function predicts the actual behavior of the tool performance indicator, or based on a determined sensitivity of the tool performance indicator to changes in the single tool parameter. The tool parameters are then ranked based on these scores, and a reduced set of critical tool parameters is derived based on the ranking. The tool performance indicator can then be modeled based on this reduced set of tool parameters.

Method and apparatus for autonomous tool parameter impact identification system for semiconductor manufacturing

A system and method for autonomously tracing a cause of particle contamination during semiconductor manufacture is provided. A contamination analysis system analyzes tool process logs together with particle contamination data for multiple process runs to determine a relationship between systematic particle contamination levels and one or more tool parameters. This relationship is used to predict expected contamination levels associated with regular usage of the tool, and to identify which tool parameters have the largest impact on expected levels of particle contamination. The contamination analysis system also identifies process logs showing unexpected deviant particle contamination levels that exceed expected contamination levels, and traces the cause of the deviant particle contamination to particular process log parameter events.

Method and apparatus for autonomous identification of particle contamination due to isolated process events and systematic trends

A system and method autonomously determines the impact of respective tool parameters on tool performance in a semiconductor manufacturing system. A parameter impact identification system receives tool parameter and performance data for one or more process runs of the semiconductor fabrication system and generates a separate function for each tool parameter characterizing the behavior of a tool performance indicator in terms of a single one of the tool parameters. Each function is then scored according to how well the function predicts the behavior of the tool performance indicator, or based on a determined sensitivity of the tool performance indicator to changes in the single tool parameter. The tool parameters are then ranked based on these scores, and a reduced set of critical tool parameters is derived based on the ranking. The tool performance indicator can then be modeled based on this reduced set of tool parameters.

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Mountain View, CA, United States of America

Aaron Archer Waterman