Tom Y Ho, 41San Francisco, CA

7954018, May 31, 2011

Feb 2, 2007

11/701700

Tom T. Ho - San Carlos CA, US
Jonathan B. Buckheit - Los Altos CA, US
Weidong Wang - Union City CA, US

Rudolph Technologies, Inc - Flanders NJ

G11C 29/00

714718, 714720, 714721, 714723, 365201

A system and method for defect analysis of multi-level memory cell devices and embedded multi-level memory in system-on-chip integrated circuits are disclosed wherein a defect data set is input into the system. When a defect data set is received, an automated test engineering system running a memory test program analyzes the defect data set to generate one or more fail bit locations and one or more fail states of the memory. The multi-level memory defect analysis system and method then classify failed bits or patterns comprising a vertical fail pattern, whereby after being classified, each memory cell failure vertical fail pattern has three data attributes comprising fail type, a number of fail bits/states, and a sequence of the fail states. The vertical fail pattern may comprise a single fail state or multi-state fail. The multi-state fail may be a continuous-states fail, discontinuous-states fail, or all-state fail.

8190952, May 29, 2012

Mar 22, 2010

12/728629

Tom T. Ho - San Carlos CA, US
Jonathan B. Buckheit - Los Altos CA, US
Weidong Wang - Union City CA, US
Xin Sun - San Jose CA, US

Rudolph Technologies, Inc. - Flanders NJ

G11C 29/00

714723

A system and method for defect analysis are disclosed wherein a defect data set is input into the system. A radius value is selected by a user, which is the maximum number of bits that bit failures can be separated from one another to be considered a bit cluster. When a defect data set is received, the system and method start with a fail bit and search for neighboring fail bits. The specified radius is used to qualify the found fail bits to be part of the bit cluster or not. If a minimum count of fail bits is not met, the system and method will stop searching and move to the next fail bit. If a minimum count of fail bits is met, the search continues for the next fail bit until the maximum fail bit count specified by the user is reached. Aggregation is provided such that once bit clusters have been classified, the number of clusters that have the exact match or partial match to each other is counted. The user may set the partial match as a threshold count to establish a match.

2007001, Jan 11, 2007

Jun 23, 2005

11/166960

Tom Ho - San Carlos CA, US
Jonathan Buckheit - Los Altos CA, US
Weidong Wang - Union City CA, US
Xin Sun - San Jose CA, US

G11C 29/00

714718000

A system and method for defect analysis are disclosed wherein a defect data set is input into the system. A radius value is selected by a user, which is the maximum number of bits that bit failures can be separated from one another to be considered a bit cluster. When a defect data set is received, the system and method start with a fail bit and search for neighboring fail bits. The specified radius is used to qualify the found fail bits to be part of the bit cluster or not. If a minimum count of fail bits is not met, the system and method will stop searching- and move to the next fail bit. If a minimum count of fail bits is met, the search continues for the next fail bit until the maximum fail bit count specified by the user is reached. Aggregation is provided such that once bit clusters have been classified, the number of clusters that have the exact match or partial match to each other is counted. The user may set the partial match as a threshold count to establish a match.

2013003, Jan 31, 2013

Jul 27, 2011

13/192387

Tom Thuy Ho - San Carlos CA, US
Weidong Wang - Union City CA, US
Woon-Kyu Choi - Seoul, KR
Ji-Hoon Keith Han - Seoul, KR
Gabriel Serge Villareal - Fresno CA, US

G06F 15/00
G06F 17/18

702179, 702185

Integrated yield/equipment data processing system for collecting and analyzing integrated tool-related data (cause data) and material-related data (effect data) pertaining to at least one material processing tool and at least one material is disclosed. In an embodiment, the tool-related data is correlated with the material-related data, and the correlated tool-related data and material-related data is employed by logic to perform at least one of root-cause analysis, prediction model building and tool control/optimization. By integrating cause-and-effect data in a single platform, the data necessary for performing, for example, automated problem detection (e.g., automated root cause analysis) and prediction, is readily available and correlated, which for example shortens the cycle time to detection and facilitates efficient and timely automated tool management and control.

2013008, Mar 28, 2013

Sep 20, 2012

13/623825

Tom Thuy Ho - San Carlos CA, US
Weidong Wang - Union City CA, US
Gabriel Serge Villareal - Fresno CA, US
Ji-Hoon Keith Han - Seoul, KR
Woon-Kyu Choi - Seoul, KR

G06N 5/02

706 50

Computer-implemented methods and systems for tool health prediction for a tool having sub-systems and components are disclosed. The method includes providing parameter values from sensors to an expert system. The method also includes providing knowledge base data from a knowledge base to the expert system. The knowledge base includes at least one of tool history, part information, domain knowledge, and model history. The method also includes generating, using the expert system, at least one tool health prediction pertaining to tool maintenance. The prediction generation employs a set of prediction models that includes at least one prediction model. The prediction generation further employs at least the parameter values and the knowledge base data.

2013017, Jul 4, 2013

Dec 29, 2011

13/340574

Tom Thuy Ho - San Carlos CA, US
Weidong Wang - Union City CA, US
Woon-Kyu Choi - Seoul, KR
Ji-Hoon Keith Han - Seoul, KR
Gabriel Serge Villareal - Fresno CA, US

G06Q 10/06

705 727

Systems and methods for automatically and/or systematically include more data sources and/or more detailed data in the analysis, prediction, and model building. Process data may be employed to pinpoint the process parameter excursions and domain knowledge and/or expert systems may be automatically and/or systematically incorporated into the root cause analysis, the prediction and/or the model building to improve results and/or to reduce the reliance on inconsistent and expensive human experts.

2014023, Aug 21, 2014

Dec 18, 2013

14/133499

Tom Thuy Ho - San Carlos CA, US
Weidong Wang - Union City CA, US

H01L 21/66

702 84

Cloud-based integrated yield/equipment data processing system for collecting and analyzing integrated tool-related data (cause data) and material-related data (effect data) pertaining to at least one material processing tool and at least one material is disclosed. In an embodiment, the tool-related data is correlated with the material-related data and the correlated tool-related data and material-related data is employed by logic to perform, using a cloud computing approach, at least one of root-cause analysis, prediction model building and tool control/optimization.