Quan Y Zhang, 732714 N Washington St, Stillwater, OK 74075

2023010, Mar 30, 2023

Feb 4, 2021

17/796751

- Veldhoven, NL
Jun TAO - Cupertino CA, US
Quan ZHANG - San Jose CA, US
Yongsheng SHU - San Jose CA, US
Wei-chun FONG - Sunnyvale CA, US

ASML NETHERLANDS B.V. - Veldhoven

G03F 1/36
G06N 3/0464
G03F 7/20

A method for determining a mask pattern and a method for training a machine learning model. The method for determining a mask pattern includes obtaining, via executing a model using a target pattern to be printed on a substrate as an input pattern, a post optical proximity correction (post-OPC) pattern; determining, based on the post-OPC pattern, a simulated pattern that will be printed on the substrate; and determining the mask pattern based on a difference between the simulated pattern and the target pattern. The determining of the mask pattern includes modifying, based on the difference, the input pattern inputted to the model such that the difference is reduced; and executing, using the modified input pattern, the model to generate a modified post-OPC pattern from which the mask pattern can be derived.

2020008, Mar 19, 2020

Sep 13, 2018

16/129996

- Walldorf, DE
Sudha Lakshman - Sunnyvale CA, US
Quan Zhang - San Jose CA, US
Sandeep Chakravarty - San Francisco CA, US
Tu Truong - San Jose CA, US
Fuming Wu - Palo Alto CA, US
Yue Li - Palo Alto CA, US
Lin Dong - Palo Alto CA, US
Richa Namballa - Palo Alto CA, US

G06F 17/30
G06F 17/21

Provided is a method and system for normalizing catalog item data to create higher quality search results. In one example, the method may include receiving a record comprising an unstructured description of an object, identifying a type of the object from among a plurality of object types and identifying a predefined attribute of the identified type of object, extracting a value from the unstructured description corresponding to the predefined attribute and modifying the extracted value to generate a normalized attribute value, and storing a structured record of the object in a structured format comprising a plurality of values of a plurality of attributes of the object from the unstructured description including the normalized attribute value for the predefined attribute of the object.

2020005, Feb 13, 2020

May 4, 2018

16/606791

- Veldhoven, NL
Yi ZOU - Foster City CA, US
Chenxi LIN - Newark CA, US
Yu CAO - Saratoga CA, US
Yen-Wen LU - Saratoga CA, US
Quan ZHANG - San Jose CA, US
Stanislas Hugo Louis BARON - San Jose CA, US
Ya LUO - Saratoga CA, US

ASML NETHERLANDS B.V. - Veldhoven

G03F 1/36
G06F 17/50
G06N 20/10
G05B 19/4097
G06K 9/46
G06T 7/60
G06T 7/00

A method including: obtaining a portion of a design layout; determining characteristics of assist features based on the portion or characteristics of the portion; and training a machine learning model using training data including a sample whose feature vector includes the characteristics of the portion and whose label includes the characteristics of the assist features. The machine learning model may be used to determine characteristics of assist features of any portion of a design layout, even if that portion is not part of the training data.

2019022, Jul 18, 2019

Mar 26, 2019

16/365471

- Armonk NY, US
Ramani R. Routray - San Jose CA, US
Quan Zhang - Detroit MI, US

G06F 11/07
G06N 7/00
G06K 9/62
G06F 17/10
G06F 17/18
G06F 11/14
G06N 3/00
G06K 9/00

One embodiment provides a method comprising receiving metadata comprising univariate time series data for each variable of a multivariate time series. The method comprises, for each variable of the multivariate time series, applying a hybrid and hierarchical model selection process to select an anomaly detection model suitable for the variable based on corresponding univariate time series data for the variable and covariations and interactions between the variable and at least one other variable of the multivariate time series, and detecting an anomaly on the variable utilizing the anomaly detection model selected for the variable. Based on each anomaly detection model selected for each variable of the multivariate time series, the method further comprises performing ensemble learning to determine whether the multivariate time series is anomalous at a particular time point.

2018018, Jul 5, 2018

Jan 3, 2017

15/397627

- Armonk NY, US
Ramani R. Routray - San Jose CA, US
Quan Zhang - Detroit MI, US

G06F 11/07
G06F 11/14

One embodiment provides a method comprising receiving metadata comprising univariate time series data for each variable of a multivariate time series. The method comprises, for each variable of the multivariate time series, applying a hybrid and hierarchical model selection process to select an anomaly detection model suitable for the variable based on corresponding univariate time series data for the variable and covariations and interactions between the variable and at least one other variable of the multivariate time series, and detecting an anomaly on the variable utilizing the anomaly detection model selected for the variable. Based on each anomaly detection model selected for each variable of the multivariate time series, the method further comprises performing ensemble learning to determine whether the multivariate time series is anomalous at a particular time point.