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公开(公告)号：US11385154B2
公开(公告)日：2022-07-12
申请号：US17034724
申请日：2020-09-28
Applicant: Tokyo Electron Limited
Inventor： Ivan Maleev , Ching Ling Meng
Abstract: Techniques herein include an apparatus and method for measuring and monitoring properties of fluids consumed in a semiconductor fabrication process. The apparatus includes a flow cell having a hollow chamber, a first chamber sidewall of the hollow chamber bisecting the length of the flow cell, the first chamber sidewall having a predetermined angle to the incoming direction of light from the first light source; a refractive index sensor configured to detect the light from the first light source transmitted through the hollow chamber of the flow cell and exiting the flow cell through the second flow cell sidewall of the at least six flow cell sidewalls; and a first light sensor configured to detect the light from the first light source scattered off the fluid in the hollow chamber.
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公开(公告)号：US11989876B2
公开(公告)日：2024-05-21
申请号：US18143949
申请日：2023-05-05
Applicant: Tokyo Electron Limited
Inventor： Shin-Yee Lu , Ivan Maleev
CPC classification number: G06T7/001 , G06N3/08 , G06T5/20 , G06T5/40 , G06T2207/20061 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.
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公开(公告)号：US11676266B2
公开(公告)日：2023-06-13
申请号：US17089158
申请日：2020-11-04
Applicant: Tokyo Electron Limited
Inventor： Shin-Yee Lu , Ivan Maleev
CPC classification number: G06T7/001 , G06N3/08 , G06T5/20 , G06T5/40 , G06T2207/20061 , G06T2207/20081 , G06T2207/20084 , G06T2207/30148
Abstract: A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.
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公开(公告)号：US11664283B2
公开(公告)日：2023-05-30
申请号：US17445570
申请日：2021-08-20
Applicant: TOKYO ELECTRON LIMITED
Inventor： Ivan Maleev
CPC classification number: H01L22/26 , B08B3/08 , B08B7/0021 , B08B7/04 , B08B13/00 , G01N21/4133 , G01N21/65 , H01L21/02057 , G01N2201/129
Abstract: An apparatus includes a measurement chamber configured to retain one or more sample substances. The apparatus includes an entrance window mounted on a side of the measurement chamber. The apparatus includes a light source configured to generate an incident light beam. The apparatus includes a Raman sensor configured to collect inelastically scattered light from the chamber, and measure an intensity of a Raman peak of a first substance from the one or more sample substances based on the collected inelastically scattered light. The apparatus further includes a processor configured to (i) calculate a concentration of the first substance based on at least the measured intensity of the Raman peak of the first substance, (ii) determine the end point of a wafer cleaning process based on a calculated concentration of the first substance, and (iii) terminate the wafer cleaning process based on the determined end point.
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