公开(公告)号：US20170040142A1

公开(公告)日：2017-02-09

申请号：US15227698

申请日：2016-08-03

Applicant: KLA-Tencor Corporation

Inventor： Hemanta Kumar Roy ,  Arpit Jain ,  Arpit Yati ,  Olivier Moreau ,  Arun Lobo

IPC: H01J37/22 ,  H01J37/28 ,  H01J37/20

CPC classification number: H01L22/00 ,  H01J2237/24571 ,  H01J2237/24592 ,  H01J2237/2817 ,  H01L22/12 ,  H01L22/20

Abstract: A technique to identify non-visual defects, such as SEM non-visual defects (SNVs), includes generating an image of a layer of a wafer, evaluating at least one attribute of the image using a classifier, and identifying the non-visual defects on the layer of the wafer. A controller can be configured to identify the non-visual defects using the classifier. This controller can communicate with a defect review tool, such as a scanning electron microscope (SEM).

Abstract translation: 识别非视觉缺陷的技术，例如SEM非视觉缺陷（SNV），包括生成晶片层的图像，使用分类器评估图像的至少一个属性，以及识别非视觉缺陷 在晶片层上。 可以将控制器配置为使用分类器识别非视觉缺陷。 该控制器可以与诸如扫描电子显微镜（SEM）的缺陷检查工具进行通信。

公开(公告)号：US11094053B2

公开(公告)日：2021-08-17

申请号：US16420408

申请日：2019-05-23

Applicant: KLA-Tencor Corporation

Inventor： Arpit Yati

IPC: G06K9/62 ,  G06T7/00 ,  G06T7/60

Abstract: A metrology system is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images of a specimen. In another embodiment, the system includes a controller configured to: receive one or more training images of a specimen from the characterization sub-system; receive one or more training region-of-interest (ROI) selections within the one or more training images; generate a machine learning classifier based on the one or more training images and the one or more training ROI selections; receive one or more product images of a specimen from the characterization sub-system; generate one or more classified regions of interest with the machine learning classifier; and determine one or more measurements of the specimen within the one or more classified regions of interest.

公开(公告)号：US10692690B2

公开(公告)日：2020-06-23

申请号：US15639311

申请日：2017-06-30

Applicant: KLA-Tencor Corporation

Inventor： Vidyasagar Anantha ,  Arpit Yati ,  Saravanan Paramasivam ,  Martin Plihal ,  Jincheng Lin

IPC: H01J37/00 ,  H01J37/244 ,  H01J37/28 ,  H01J37/20

Abstract: Use of care areas in scanning electron microscopes or other review tools can provide improved sensitivity and throughput. A care area is received at a controller of a scanning electron microscope from, for example, an inspector tool. The inspector tool may be a broad band plasma tool. The care area is applied to a field of view of a scanning electron microscope image to identify at least one area of interest. Defects are detected only within the area of interest using the scanning electron microscope. The care areas can be design-based or some other type of care area. Use of care areas in SEM tools can provide improved sensitivity and throughput.

公开(公告)号：US20190004504A1

公开(公告)日：2019-01-03

申请号：US15814561

申请日：2017-11-16

Applicant: KLA-Tencor Corporation

Inventor： Arpit Yati

IPC: G05B19/418 ,  G05B13/02

Abstract: An initial inspection or critical dimension measurement can be made at various sites on a wafer. The location, design clips, process tool parameters, or other parameters can be used to train a deep learning model. The deep learning model can be validated and these results can be used to retrain the deep learning model. This process can be repeated until the predictions meet a detection accuracy threshold. The deep learning model can be used to predict new probable defect location or critical dimension failure sites.

公开(公告)号：US20170228866A1

公开(公告)日：2017-08-10

申请号：US15258546

申请日：2016-09-07

Applicant: KLA-Tencor Corporation

Inventor： Arpit Jain ,  Arpit Yati ,  Thirupurasundari Jayaraman ,  Raghavan Konuru ,  Raj Kuppa ,  Hema Prasad ,  Saiyashwanth Momula ,  Arun Lobo

IPC: G06T7/00 ,  G01N23/225 ,  H01L21/66 ,  H01J37/22

CPC classification number: H01L22/20 ,  G01N23/2251 ,  G01N2223/6116 ,  G06T3/608 ,  G06T7/001 ,  G06T2207/10061 ,  G06T2207/30148 ,  H01L22/12

Abstract: Deskew for image review, such as SEM review, aligns inspection and review coordinate systems. Deskew can be automated using design files or inspection images. A controller that communicates with a review tool can align a file of the wafer, such as a design file or an inspection image, to an image of the wafer from the review tool; compare alignment sites of the file to alignment sites of the image from the review tool; and generate a deskew transform of coordinates of the alignment sites of the file and coordinates of alignment sites of the image from the review tool. The image of the wafer may not contain defects.