Ajay K Baranwal from 2298 W Cantara Dr, Pleasanton, CA 94568, age 48

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Method For Computational Metrology And Inspection For Patterns To Be Manufactured On A Substrate
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US Patent:

20230037918, Feb 9, 2023
Filed:

Aug 2, 2022
Appl. No.:

17/816910
Inventors:

- San Jose CA, US
Jocelyn Blair - Campbell CA, US
Ajay Baranwal - Dublin CA, US
Assignee:

D2S, Inc. - San Jose CA
International Classification:

G06T 7/00
G06T 7/70
G03F 1/84
G06N 3/04
Abstract:

Methods include generating a scanner aerial image using a neural network, where the scanner aerial image is generated using a mask inspection image that has been generated by a mask inspection machine. Embodiments also include training the neural network with a set of images, such as with a simulated scanner aerial image and another image selected from a simulated mask inspection image, a simulated Critical Dimension Scanning Electron Microscope (CD-SEM) image, a simulated scanner emulator image and a simulated actinic mask inspection image.

Methods And Systems For Compressing Shape Data For Electronic Designs
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US Patent:

20200273210, Aug 27, 2020
Filed:

Feb 18, 2020
Appl. No.:

16/793152
Inventors:

- San Jose CA, US
Ajay Baranwal - Dublin CA, US
Michael J. Meyer - Palo Alto CA, US
Assignee:

Center for Deep Learning in Electronics Manufacturing, Inc. - San Jose CA
International Classification:

G06T 9/00
Abstract:

Methods for compressing shape data for a set of electronic designs include inputting a set of shape data, where the shape data represents a set of shapes for a device fabrication process. A convolutional autoencoder is used on the set of shape data, the convolutional autoencoder having a pre-determined set of convolution layers including a kernel size and filter size for each convolution layer. The set of shape data is encoded to compress the set of shape data, using the pre-determined set of convolution layers of the convolutional autoencoder, to create a set of encoded shape data. The set of shape data comprises an SEM image, and the encoded set of shape data identifies a mask defect.

Systems And Methods Providing A Cognitive Augmented Memory Network
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US Patent:

20200057807, Feb 20, 2020
Filed:

Aug 20, 2019
Appl. No.:

16/545168
Inventors:

- Valencia CA, US
Ajay Baranwal - Dublin CA, US
Deepak Jain - Murphy TX, US
International Classification:

G06F 17/27
G06F 16/34
G06N 3/08
Abstract:

A system to electronically generate original content may include a Cognitive Memory Augmented Network (“CAMN”) that ingests data from structured and unstructured sources and organizes it in a neural network. Generic and/or custom decomposition may ensure that the data sources are broken down inside the CAMN to individual elements of reusable data. A Cognitive Gateway Interface (“CGI”) may make data available inside the CAMN accessible to processes such as cognitive search, content extraction, and/or summarization. A feedback mechanism may ingest human thought and convert the feedback to introduce original content into an output. With an enriched CAMN built upon substantial digital content, the system may learn deep semantic meaning and understanding based on content. The system may create and curate new articles, and an assistant system may work as interpreter of content. The system may help with complex research on advanced topics and provide personalized and/or customized reports.

Methods And Systems For Forming A Pattern On A Surface Using Multi-Beam Charged Particle Beam Lithography
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US Patent:

20200051781, Feb 13, 2020
Filed:

Oct 17, 2019
Appl. No.:

16/655582
Inventors:

- San Jose CA, US
Thang Nguyen - San Jose CA, US
Ajay Baranwal - Dublin CA, US
Michael J. Meyer - Palo Alto CA, US
Suhas Pillai - San Jose CA, US
Assignee:

D2S, Inc. - San Jose CA
International Classification:

H01J 37/317
Abstract:

Methods for fracturing or mask data preparation are disclosed in which a set of single-beam charged particle beam shots is input; a calculated image is calculated using a neural network, from the set of single-beam charged particle beam shots; and a set of multi-beam shots is generated based on the calculated image, to convert the set of single-beam charged particle beam shots to the set of multi-beam shots which will produce a surface image on the surface. Methods for training a neural network include inputting a set of single-beam charged particle beam shots; calculating a set of calculated images using the set of single-beam charged particle beam shots; and training the neural network with the set of calculated images.