Jessie M Burger

US Patent:

20120242667, Sep 27, 2012

Filed:

Mar 21, 2011

Appl. No.:

13/052943

Inventors:

Sanjeev Kaushal - San Jose CA, US
Kenji Sugishima - Tokyo, JP
Sukesh Janubhai Patel - Cupertino CA, US
Robert Filman - Menlo Park CA, US
Wolfgang Polak - Sunnyvale CA, US
Orion Wolfe - Oakland CA, US
Jessie Burger - Mountain View CA, US

Assignee:

TOKYO ELECTRON LIMITED - Tokyo

International Classification:

G06T 11/20

US Classification:

345440

Abstract:

The subject disclosure relates to automatically learning relationships among a plurality of manufacturing tool parameters as applied to arbitrary semiconductor manufacturing tools and a graphical user interface that is supported, at least in part, by an autonomous learning system. The graphical user interface can create one or more matrixes based on received data and can further generate additional matrices by transforming the one or more matrixes. A series of windows can be output, wherein the series of windows, provide performance analysis that comprises a matching between a focus chamber and a reference chamber. In an aspect, the focus chamber and the reference chamber can be different chambers. In another aspect, the focus chamber and the reference chamber can be the same chamber, which provides analysis of the deterioration in performance of the same chamber over time.

US Patent:

20180121619, May 3, 2018

Filed:

Oct 31, 2016

Appl. No.:

15/339803

Inventors:

- Burlingame CA, US
Aaron Archer Waterman - Mountain View CA, US
Jessie M. Burger - Mountain View CA, US
Armando Franco - San Francisco CA, US

International Classification:

G06F 19/00
G06F 17/30

Abstract:

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.