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US Patent 11966212 Spectrographic monitoring using a neural network

Patent 11966212 was granted and assigned to Applied Materials on April, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
Applied Materials
Applied Materials
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Current Assignee
Applied Materials
Applied Materials
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119662120
Patent Inventor Names
Benjamin Cherian0
Date of Patent
April 23, 2024
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Patent Application Number
169840010
Date Filed
August 3, 2020
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Patent Citations
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US Patent 7052921 System and method using in situ scatterometry to detect photoresist pattern integrity during the photolithography process
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US Patent 7427457 Methods for designing grating structures for use in situ scatterometry to detect photoresist defects
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US Patent 7656528 Periodic patterns and technique to control misalignment between two layers
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US Patent 7764377 Spectrum based endpointing for chemical mechanical polishing
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US Patent 7930058 Nanotopography control and optimization using feedback from warp data
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US Patent 8024676 Multi-pitch scatterometry targets
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US Patent 9551567 Reducing noise in spectral data from polishing substrates
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US Patent 9833874 Applying dimensional reduction to spectral data from polishing substrates
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Patent Primary Examiner
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Kenneth M Lo
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CPC Code
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G06N 3/084
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G06N 3/0454
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G06F 17/12
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H01L 21/67242
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H01L 22/12
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H01L 22/30
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G05B 13/027
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H01L 21/67092
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Patent abstract

A method of processing a substrate includes subjecting a substrate to processing that modifies a thickness of an outer layer of the substrate, measuring a spectrum of light reflected from the substrate during processing, reducing the dimensionality of the measured spectrum to generate a plurality of component values, generating a characterizing value using an artificial neural network, and determining at least one of whether to halt processing of the substrate or an adjustment for a processing parameter based on the characterizing value. The artificial neural network has a plurality of input nodes to receive the plurality of component values, an output node to output the characterizing value, and a plurality of hidden nodes connecting the input nodes to the output node.

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