The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 30, 2025

Filed:

Feb. 14, 2020
Applicant:

Quantum Silicon Inc., Edmonton, CA;

Inventors:

Mohammad Rashidi, Edmonton, CA;

Jeremiah Croshaw, Edmonton, CA;

Robert Wolkow, Edmonton, CA;

Assignee:

Quantum Silicon Inc., Edmonton, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G01Q 30/04 (2010.01); G01Q 60/10 (2010.01);
U.S. Cl.
CPC ...
G06T 7/0008 (2013.01); G01Q 30/04 (2013.01); G01Q 60/10 (2013.01); G06T 2207/10061 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30148 (2013.01);
Abstract

A method for autonomously applying a dangling bond pattern to a substrate for atom scale device fabrication includes inputting the pattern, initiating a patterning process, scanning the substrate using a scanning probe microscope (SPM) to generate an SPM image of the substrate, feeding the SPM image into a trained convolution neural network (CNN), analyzing the SPM image using the CNN to identify substrate defects, determining a defect free substrate area for pattern application; and applying the pattern to the substrate in that area. An atom scale electronic component includes functional patches on a substrate and wires electrically connecting the functional patches. Training a CNN includes recording a Scanning Tunneling Microscope (STM) image of the substrate, extracting images of defects from the STM image, labeling pixel-wise the defect images, and feeding the extracted and labeled images of defects into a CNN to train the CNN for semantic segmentation.


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