Xiaochun LiCupertino, CA

6944360, Sep 13, 2005

Jan 29, 2004

10/768270

Xiaochun Li - Stanford CA, US
Friedrich Prinz - Woodside CA, US
Anastasios Golnas - San Francisco CA, US

The Board of Trustees of the Leland Stanford Junior University - Stanford CA

G02B006/00
G02B006/44
G01J005/08
G01K011/32

385 12, 385107, 385128, 25022713, 25022718, 374161

A sensor embedded in a high temperature metal is incorporated into a sensing system for measuring temperature, strain, or other properties of a metal structure. An optical system transmits light to and receives output signals from the sensor for analysis. With rotating structures, an optical fiber lead transmits light between the sensor and external surface of the structure along its rotational axis, allowing the lead to remain fixed with respect to the optical system as the structure rotates at high speeds.

8103086, Jan 24, 2012

Aug 30, 2010

12/871821

Ruifang Shi - Cupertino CA, US
Xiaochun Li - San Jose CA, US

KLA-Tencor Corporation - Milpitas CA

G06K 9/62

382144

Provided are novel inspection methods and systems for inspecting photomasks to identify various defects using a model-based approach and information obtained from modeled images. Modeled or simulation images are generated directly from test or reference images. Some examples include aerial images that represent expected patterns projected by a lithography system on a substrate as well as photoresist images that represent expected resist patterns. Test images are first represented as a band limited mask pattern, which may include only linear terms for faster image processing. This pattern is then used to construct a modeled image, which in turn is used to construct a model-based feature map. This map serves as a base for inspecting the original test images to identify photomask defects and may include information that allows differentiating between various feature types based on their lithographic significance and other characteristics.

6876785, Apr 5, 2005

Jun 20, 2000

09/597966

Xiaochun Li - Stanford CA, US
Friedrich Prinz - Woodside CA, US
Anastasios Golnas - San Francisco CA, US

The Board of Trustees of the Leland Stanford Junior University - Stanford CA

G02B006/26
G02B006/22

385 12, 385128

A method for embedding fiber optic sensors in a high melting temperature metal structure produces embedded sensors that are uniformly and closely bonded with the metal and do not slip upon metal expansion and contraction. The structure is built in layers onto the sensor. On top of a first thin sputter-coated metallic layer, approximately 1-3 μm thick, is electroplated a second thin layer, approximately 0. 25-2 mm thick. Finally, a metal structure is built around the thin metallic layers by laser cladding, casting, welding, or other method.

2022030, Sep 22, 2022

Mar 16, 2021

17/203719

- Milpitas CA, US
Jin Qian - Shanghai, CN
Zhuang Liu - Shanghai, CN
Xiaochun Li - San Jose CA, US
Siqing Nie - Shanghai, CN

G06T 7/00
G06T 7/11

A rendered image is generated from a semiconductor device design file. The rendered image is segmented based on a grey level of the rendered image. Care areas are determined based on the segmenting. Defect inspection is performed in the care areas. This process can be performed on a wafer inspection tool that uses photon optics or electron beam optics.

2022024, Aug 4, 2022

Apr 18, 2022

17/722710

- Milpitas CA, US
Hucheng Lee - Cupertino CA, US
Sangbong Park - Dublin CA, US
Xiaochun Li - San Jose CA, US

G06T 7/00
H01L 27/108
H01L 27/11556
G06T 7/11
G01N 21/95
G01N 21/956
H01L 27/11582

With the disclosed systems and methods for DRAM and 3D NAND inspection, an image of the wafer is received based on the output for an inspection tool. Geometric measurements of a design of a plurality of memory devices on the wafer are received. A care area with higher inspection sensitivity is determined based on the geometric measurements.

2021038, Dec 9, 2021

May 28, 2021

17/334179

- Milpitas CA, US
Huan Jin - Dublin CA, US
Xiaochun Li - San Jose CA, US

G06T 7/32
G06T 7/00
G06T 7/33

Methods and systems for determining one or more alignment parameters for use in a process performed on a specimen are provided. One method includes determining measures of similarity between images generated by an imaging system for corresponding locations in each of two or more pairs of dies on a specimen and performing cluster analysis based on the determined measures of similarity to identify the images that are most similar to each other and to assign different subsets of the images that are most similar to each other to different die clusters, respectively. The method also includes separately determining one or more alignment parameters for two or more of the different die clusters. The one or more alignment parameters are used for aligning images generated by the imaging system for the specimen or another specimen to a common reference.

2021020, Jul 8, 2021

Jan 3, 2020

16/733485

- Milpitas CA, US
Lin Yang - Milpitas CA, US
Xiaochun Li - San Ramon CA, US
Yequn Zhang - San Jose CA, US
Yongxiong Ren - San Jose CA, US
Yinbo Shi - Santa Clara CA, US
Patrick Dong - San Jose CA, US

Gyrfalcon Technology Inc. - Milpitas CA

G06T 11/60
G06N 3/08
G06N 3/063
G06N 5/04
G06N 20/00

In some embodiments, a system includes an artificial intelligence (AI) chip and a processor coupled to the AI chip and configured to receive an input image, crop the input image into a plurality of cropped images, and execute the AI chip to produce a plurality of feature maps based on at least a subset of the plurality of cropped images. The system may further merge at least a subset of the plurality of feature maps to form a merged feature map, and produce an output image based on the merged feature map. The cropping and merging operations may be performed according to a same pattern. The system may also include a training network configured to train weights of the CNN model in the AI chip in a gradient descent network. Cropping and merging may be performed over the training sample images in the training work in a similar manner.

2021009, Apr 1, 2021

Sep 27, 2019

16/586543

- Milpitas CA, US
Bin Yang - San Jose CA, US
Qi Dong - San Jose CA, US
Xiaochun Li - San Ramon CA, US
Wenhan Zhang - Mississauga, CA
Yequn Zhang - San Jose CA, US
Hua Zhou - San Jose CA, US
Patrick Dong - San Jose CA, US

Gyrfalcon Technology Inc. - Milpitas CA

G06K 9/00
G06F 16/738
G06F 16/74
G06N 3/04
G06N 3/063

A video retrieval system may include a feature extractor configured to extract first feature descriptors for multiple image frames in the query video. The system may also include a feature extractor to extract second feature descriptors for multiple image frames in a candidate video in a video database. The system may include a comparator to compare the first and second feature descriptors to determine a subset of image frames in the candidate video that are similar to the first video. The system may output die query output by displaying the subset of image frames in a slide show. The system may also output the query by displaying a video formed by at least the subset of image frames. The feature extractor may be implemented in a convolution neural network (CNN) in an artificial intelligence (AI) chip. The system may include key frame extractor to detect key frames in the video.