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Sandeep S Bhagwat

age ~54

from Milpitas, CA

Also known as:
  • Sandeep Bhagwat
  • Sandeep Bhagway
  • Bhagwat A
Phone and address:
1224 Calle De Cuestanada, Milpitas, CA 95035
408-7191207

Sandeep Bhagwat Phones & Addresses

  • 1224 Calle De Cuestanada, Milpitas, CA 95035 • 408-7191207
  • 2250 Monroe St, Santa Clara, CA 95050 • 408-2416770
  • Santa Cruz, CA
  • 1224 Calle De Cuestanada, Milpitas, CA 95035 • 619-2000989

Work

  • Position:
    Professional/Technical

Education

  • Degree:
    Graduate or professional degree

Emails

Resumes

  • Sandeep Bhagwat Photo 1

    Senior Software Engineering Manager

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  • Location:
    Milpitas, CA
  • Industry:
    Semiconductors
  • Work:
    KLA-Tencor - San Francisco Bay Area since Sep 2010
    Software Engineering Manager

    KLA-Tencor since Sep 2010
    Staff Software Design Engineer
  • Education:
    Indian Institute of Science 1989 - 1991
    M. tech, CEDT
    K J Somaiya College Of Engineering 1985 - 1989
    Bachelor of Engineering (B.E.), Electronics Engineering
  • Skills:
    Software Engineering
    Semiconductors
    Software Design
    Debugging
    Software Development
    Engineering Management
    Embedded Systems
    C++
    System Architecture
    Algorithms
    Embedded Software
    Ic
    Clearcase
    C
    Electronics
    Product Management
    Semiconductor Industry
    Multithreading
    Mfc
    Device Drivers
    Product Engineering
    Rtos
    Hardware Architecture

Us Patents

  • Data Analysis Flow Engine

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  • US Patent:
    7093207, Aug 15, 2006
  • Filed:
    Nov 17, 2003
  • Appl. No.:
    10/715082
  • Inventors:
    Po-Shou Liao - Keelung, TW
    Benny Huang - Jhonghe, TW
    Charles Lai - Taipei, TW
    Jimmy Liao - Erlin Township, TW
    Bing Li - Fremont CA, US
    Peter Eldredge - Scotts Valley CA, US
    Leslie F. Smith - Puyallup WA, US
    Felix Lai - Jhongjheng District, TW
    Sino Ho - Hsinchu, TW
    Ellis E-Li Chang - Saratoga CA, US
    Sandeep Bhagwat - Milpitas CA, US
    Anthony Cheung - Milpitas CA, US
    Michael J. Bellon - Gilroy CA, US
  • Assignee:
    KLA-Tencor Technologies Corporation - Milpitas CA
  • International Classification:
    G06F 17/50
    G06F 12/00
    G06F 19/00
  • US Classification:
    716 1, 707203, 700121
  • Abstract:
    An extensible data analysis system for analyzing integrated circuit fabrication data produced during integrated circuit fabrication, including an application tier that selectively runs analysis nodes. The application tier has an architecture for optionally including and excluding a desired selection of the analysis nodes. The application tier architecture allows the selection of the analysis nodes to be dynamically added by a user. A data access tier selectively runs data reader nodes. The data access tier has an architecture for optionally including and excluding a desired selection of the data reader nodes. The data reader nodes interpret a desired variety of data source files containing the integrated circuit fabrication data having different formats for access by the application tier. The data access tier architecture allows the selection of the data reader nodes to be dynamically added by the user.
  • Flexible Hybrid Defect Classification For Semiconductor Manufacturing

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  • US Patent:
    7142992, Nov 28, 2006
  • Filed:
    Sep 30, 2004
  • Appl. No.:
    10/954968
  • Inventors:
    Patrick Huet - San Jose CA, US
    Maruti Shanbhag - Bangalore, IN
    Sandeep Bhagwat - Milpitas CA, US
    Michal Kowalski - Santa Cruz CA, US
    Vivekanand Kini - Sunnyvale CA, US
    David Randall - Sunnyvale CA, US
    Sharon McCauley - San Jose CA, US
    Tong Huang - Sunnyvale CA, US
    Jianxin Zhang - Santa Clara CA, US
    Kenong Wu - Davis CA, US
    Lisheng Gao - Morgan Hill CA, US
    Ariel Tribble - Fremont CA, US
    Ashok Kulkarni - San Jose CA, US
    Cecelia Anne Campochiaro - Sunnyvale CA, US
  • Assignee:
    KLA-Tencor Technologies Corp. - Milpitas CA
  • International Classification:
    G01R 31/00
    G06F 19/00
  • US Classification:
    702 58
  • Abstract:
    Hybrid methods for classifying defects in semiconductor manufacturing are provided. The methods include applying a flexible sequence of rules for defects to inspection data. The sequence of rules includes deterministic rules, statistical rules, hybrid rules, or some combination thereof. The rules included in the sequence may be selected by a user using a graphical interface. The method also includes classifying the defects based on results of applying the sequence of rules to the inspection data.
  • Spatial Signature Analysis

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  • US Patent:
    6718526, Apr 6, 2004
  • Filed:
    Feb 7, 2003
  • Appl. No.:
    10/360433
  • Inventors:
    Peter Eldredge - Scotts Valley CA
    Patrick Y. Huet - San Jose CA
    Robinson Piramuthu - San Jose CA
    Sandeep Bhagwat - Milpitas CA
    Kai Chi - San Jose CA
    Kai Liu - Santa Clara CA
    Martin Plihal - Pleasanton CA
    Shaio Roan - Cupertino CA
    Maruti Shanbhag - Sunnyvale CA
  • Assignee:
    KLA-Tencor Corporation - Milpitas CA
  • International Classification:
    G06F 1750
  • US Classification:
    716 4, 716 5
  • Abstract:
    A system for determining an assigned classification for a set of physical events on a substrate. Sensors sense the physical events on the substrate and produce event data. A plug in rule module manager receives and manages any number of plug in rule modules. Each plug in rule module has an input, a local filter, an analyzer, and an output. The input receives the event data and confidence values from preceding plug in rule modules. The local filter analyzes the received confidence values from the preceding plug in rule modules and selectively by passes the plug in rule module based at least in part upon the received confidence values from the preceding plug in rule modules. The analyzer analyzes the event data in view of a given classification associated with the plug in rule module, and assigns a confidence value based at least in part upon how well the event data fits the given classification. The output provides the confidence value to subsequent plug in rule modules. A post processor receives the confidence values provided by the plug in rule modules, and makes a final selection of the assigned classification to be associated with the set of physical events from the given classifications based at least in part upon a comparison of the confidence values produced by all plug in rule modules.

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Youtube

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sandeep bhagwat

  • Category:
    Entertainment
  • Uploaded:
    20 Feb, 2011
  • Duration:
    5m 25s

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