公开(公告)号：US20190198287A1

公开(公告)日：2019-06-27

申请号：US15851632

申请日：2017-12-21

Applicant: FEI Company

Inventor： Tomás Vystavel ,  Libor Strakos ,  Anna Prokhodtseva ,  Jaromír Vanhara ,  Jaroslav Stárek

IPC: H01J37/20 ,  H01J37/244 ,  H01J37/147 ,  H01J37/28 ,  H01J37/305

CPC classification number: H01J37/20 ,  H01J37/1478 ,  H01J37/244 ,  H01J37/28 ,  H01J37/3045 ,  H01J37/305 ,  H01J2237/1501 ,  H01J2237/20207 ,  H01J2237/24475 ,  H01J2237/2802 ,  H01J2237/30455 ,  H01J2237/3174 ,  H01J2237/31747

Abstract: A substrate is alignable for ion beam milling or other inspection or processing by obtaining an electron channeling pattern (ECP) or other electron beam backscatter pattern from the substrate based on electron beam backscatter from the substrate. The ECP is a function of substrate crystal orientation and tilt angles associated with ECP pattern values at or near a maximum, minimum, or midpoint are used to determine substrate tilt. Such tilt is then compensated or eliminated using a tilt stage coupled the substrate, or by adjusting an ion beam axis. In typical examples, circuit substrate “chunks” are aligned for ion beam milling to reveal circuit features for evaluation of circuit processing.

公开(公告)号：US11650171B2

公开(公告)日：2023-05-16

申请号：US17357409

申请日：2021-06-24

Applicant: FEI Company

Inventor： Han Han ,  Libor Strakos ,  Thomas Hantschel ,  Tomas Vystavel ,  Clement Porret

IPC: H01J37/244 ,  G01N23/203 ,  H01J37/28

CPC classification number: G01N23/203 ,  H01J37/244 ,  H01J37/28 ,  G01N2223/6116

Abstract: Methods and apparatus determine offcut angle of a crystalline sample using electron channeling patterns (ECPs), wherein backscattered electron intensity exhibits angular variation dependent on crystal orientation. A zone axis normal to a given crystal plane follows a circle as the sample is azimuthally rotated. On an ECP image presented with tilt angles as axes, the radius of the circle is the offcut angle of the sample. Large offcut angles are determined by a tilt technique that brings the zone axis into the ECP field of view. ECPs are produced with a scanning electron beam and a monolithic backscattered electron detector; or alternatively with a stationary electron beam and a pixelated electron backscatter diffraction detector. Applications include strain engineering, process monitoring, detecting spatial variations, and incoming wafer inspection. Methods are 40× faster than X-ray diffraction. 0.01-0.1° accuracy enables semiconductor applications.

公开(公告)号：US20220412900A1

公开(公告)日：2022-12-29

申请号：US17357409

申请日：2021-06-24

Applicant: FEI Company

Inventor： Han Han ,  Libor Strakos ,  Thomas Hantschel ,  Tomas Vystavel ,  Clement Porret

IPC: G01N23/203 ,  H01J37/28 ,  H01J37/244

Abstract: Methods and apparatus determine offcut angle of a crystalline sample using electron channeling patterns (ECPs), wherein backscattered electron intensity exhibits angular variation dependent on crystal orientation. A zone axis normal to a given crystal plane follows a circle as the sample is azimuthally rotated. On an ECP image presented with tilt angles as axes, the radius of the circle is the offcut angle of the sample. Large offcut angles are determined by a tilt technique that brings the zone axis into the ECP field of view. ECPs are produced with a scanning electron beam and a monolithic backscattered electron detector; or alternatively with a stationary electron beam and a pixelated electron backscatter diffraction detector. Applications include strain engineering, process monitoring, detecting spatial variations, and incoming wafer inspection. Methods are 40× faster than X-ray diffraction. 0.01-0.1° accuracy enables semiconductor applications.

公开(公告)号：US20200034956A1

公开(公告)日：2020-01-30

申请号：US16045702

申请日：2018-07-25

Applicant: FEI Company

Inventor： Ondrej Machek ,  Tomás Vystavêl ,  Libor Strakos ,  Pavel Potocek

IPC: G06T7/00 ,  G06N3/08 ,  G06K9/62

Abstract: Techniques for training an artificial neural network (ANN) using simulated specimen images are described. Simulated specimen images are generated based on data models. The data models describe characteristics of a crystalline material and characteristics of one or more defect types. The data models do not include any image data. Simulated specimen images are input as training data into a training algorithm to generate an artificial neural network (ANN) for identifying defects in crystalline materials. After the ANN is trained, the ANN analyzes captured specimen images to identify defects shown therein.