Asml Netherlands B.v.

Hermes Microvision Inc.

Hermes Microvision, Inc. (taiwan)

Wei Fang

Jack Jau

Lingling Pu

Zhao-Li Zhang

Hong Xiao

Zhong-wei Chen

Zhonghua Dong

Fei Wang

Chuan Li

Zhaohui Guo

Wentian Zhou

Yan Zhao

Teng Wang

Zhichao Chen

Xuedong Liu

Joe Wang

Kuo-Shih Liu

Kevin Liu

Liangjiang Yu

Shih-Tsung Chen

Weiming Ren

Zhongwei Chen

Ming Xu

Chiyan Kuan

You-Jin Wang

Futang Peng

Yi-Ying Wang

Pang-Hsuan Huang

Shao-Wei Lai

Cho Huak Teh

Shih-Tsung Hsu

Bo Wang

Haili Zhang

Ruochong Fei

Jian-Min Liao

Nan Zhao

Stefan Hunsche

Yongxin Wang

Chung-Shih Pan

Yu Liu

Zhaoli Zhang

Ying Luo

Yi Xiang Wang

Chester Kuo

Long Ma

Fuming Wang

Chih-Yu Jen

Steve Lin

Bohang Zhu

Long Di

Nianpei Deng

Peilei Zhang

Ju Hao Chien

Pengcheng Zhang

Hairong Lei

Eric Ma

Jon Chiang

Yu-Tsorng Fu

Chad Liao

Alina Wang

Robeter Jian

Shengcheng Jin

Ruoyu Zhu

Zhengwei Zhou

Xuehui Yin

Systems and methods for irradiating a sample with a charged-particle beam are disclosed. The charged-particle beam system may comprise a stage configured to hold a sample and is movable in at least one of X-Y-Z axes. The charged-particle beam system may further comprise a position sensing system to determine a lateral and vertical displacement of the stage, and a beam deflection controller configured to apply a first signal to deflect a primary charged-particle beam incident on the sample to at least partly compensate for the lateral displacement, and to apply a second signal to adjust a focus of the deflected charged-particle beam incident on the sample to at least partly compensate for the vertical displacement of the stage. The first and second signals may comprise an electrical signal having a high bandwidth in a range of 10 kHz to 50 kHz, and 50 kHz to 200 kHz, respectively.

Systems and methods for focusing charged—particle beams

Inspection systems and methods are disclosed. An inspection system may include a first energy source configured to provide a first landing energy beam and a second energy source configured to provide a second landing energy beam. The inspection system may also include a beam controller configured to selectively deliver one of the first and second landing energy beams towards a same field of view, and to switch between delivery of the first and second landing energy beams according to a mode of operation of the inspection system.

Multiple landing energy scanning electron microscopy systems and methods

Apparatuses, methods, and systems for ultra-fast beam current adjustment for a charged-particle inspection system include an charged-particle source configured to emit charged particles for scanning a sample; and an emission booster configured to configured to irradiate electromagnetic radiation onto the charged-particle source for boosting charged-particle emission in a first cycle of a scanning operation of the charged-particle inspection system, and to stop irradiating the electromagnetic radiation in a second cycle of the scanning operation.

Beam current adjustment for charged-particle inspection system

Disclosed herein is a method of automatically obtaining training images to train a machine learning model that improves image quality. The method may comprise analyzing a plurality of patterns of data relating to a layout of a product to identify a plurality of training locations on a sample of the product to use in relation to training the machine learning model. The method may comprise obtaining a first image having a first quality for each of the plurality of training locations, and obtaining a second image having a second quality for each of the plurality of training locations, the second quality being higher than the first quality. The method may comprise using the first image and the second image to train the machine learning model.

Fully automated SEM sampling system for e-beam image enhancement

A method for correcting metrology data of a patterning process. The method includes obtaining (i) metrology data of a substrate subjected to the patterning process and (ii) a quality metric (e.g., a focus index) that quantifies a quality of the metrology data of the substrate; establishing a correlation between the quality metric and the metrology data; and determining a correction to the metrology data based on the correlation between the quality metric and the metrology data.

Metrology data correction using image quality metric

A system and method for defect inspection using voltage contrast in a charged particle system are provided. Some embodiments of the system and method include positioning the stage at a first position to enable a first beam of the plurality of beams to scan a first surface area of the wafer at a first time to generate a first image associated with the first surface area; positioning the stage at a second position to enable a second beam of the plurality of beams to scan the first surface area at a second time to generate a second image associated with the first surface area; and comparing the first image with the second image to enable detecting whether a defect is identified in the first surface area of the wafer.

System and method for defect inspection using voltage contrast in a charged particle system

An improved apparatus and method for enhancing an image, and more particularly an apparatus and method for enhancing an image through cross-talk cancellation in a multiple charged-particle beam inspection are disclosed. An improved method for enhancing an image includes acquiring a first image signal of a plurality of image signals from a detector of a multi-beam inspection system. The first image signal corresponds to a detected signal from a first region of the detector on which electrons of a first secondary electron beam and of a second secondary electron beam are incident. The method includes reducing, from the first image signal, cross-talk contamination originating from the second secondary electron beam using a relationship between the first image signal and beam intensities associated with the first secondary electron beam and the second secondary electron beam. The method further includes generating a first image corresponding to first secondary electron beam after reduction.

Cross-talk cancellation in multiple charged-particle beam inspection

Systems and methods for in-die metrology using target design patterns are provided. These systems and methods include selecting a target design pattern based on design data representing the design of an integrated circuit, providing design data indicative of the target design pattern to enable design data derived from the target design pattern to be added to second design data, wherein the second design data is based on the first design data. Systems and methods can further include causing structures derived from the second design data to be printed on a wafer, inspecting the structures on the wafer using a charged-particle beam tool, and identifying metrology data or process defects based on the inspection. In some embodiments the systems and methods further include causing the charged-particle beam tool, the second design data, a scanner, or photolithography equipment to be adjusted based on the identified metrology data or process defects.

In-die metrology methods and systems for process control

An inspection method includes the following steps: identifying a plurality of patterns within an image; and comparing the plurality of patterns with each other for measurement values thereof. The above-mentioned inspection method uses the pattern within the image as a basis for comparison; therefore, measurement values of the plurality of pixels constructing the pattern can be processed with statistical methods and then compared, and the false rate caused by variation of a few pixels is decreased significantly. An inspection system implementing the above-mentioned method is also disclosed.

Inspection method and system

A defect inspection system is disclosed. According to certain embodiments, the system includes a memory storing instructions implemented as a plurality of modules. Each of the plurality of modules is configured to detect defects having a different property. The system also includes a controller configured to cause the computer system to: receive inspection data representing an image of a wafer; input the inspection data to a first module of the plurality of modules, the first module outputs a first set of points of interests (POIs) having a first property; input the first set of POIs to a second module of the plurality of modules, the second module output a second set of POIs having the second property; and report that the second set of POIs as defects having both the first property and the second property.

Cascade defect inspection

A method for aligning a wafer image with a reference image, comprising: searching for a targeted reference position on the wafer image for aligning the wafer image with the reference image; and in response to a determination that the targeted reference position does not exist: defining a current lock position and an area that encloses the current lock position on the wafer image; computing an alignment score of the current lock position; comparing the alignment score of the current lock position with stored alignment scores of positions previously selected in relation to aligning the wafer image with the reference image; and aligning the wafer image with the reference image based on the comparison.

Method and apparatus for adaptive alignment

A wafer inspection system includes a controller in communication with an electron-beam inspection tool. The controller includes circuitry to: acquire, via an optical imaging tool, coordinates of defects on a sample; set a Field of View (FoV) of the electron-beam inspection tool to a first size to locate a subset of the defects; determine a position of each defect of the subset of the defects based on inspection data generated by the electron-beam inspection tool during a scanning of the sample; adjust the coordinates of the defects based on the determined positions of the subset of the defects; and set the FoV of the electron-beam inspection tool to a second size to locate additional defects based on the adjusted coordinates.

System and method for bare wafer inspection

A pattern grouping method may include receiving an image of a first pattern, generating a first fixed-dimensional feature vector using trained model parameters applying to the received image, and assigning the first fixed-dimensional feature vector a first bucket ID. The method may further include creating a new bucket ID for the first fixed-dimensional feature vector in response to determining that the first pattern does not belong to one of a plurality of buckets corresponding to defect patterns, or mapping the first fixed-dimensional feature vector to the first bucket ID in response to determining that the first pattern belongs to one of a plurality of buckets corresponding to defect patterns.

Pattern grouping method based on machine learning

Systems and methods for optimal electron beam metrology guidance are disclosed. According to certain embodiments, the method may include receiving an acquired image of a sample, determining a set of image parameters based on an analysis of the acquired image, determining a set of model parameters based on the set of image parameters, generating a set of simulated images based on the set of model parameters. The method may further comprise performing measurement of critical dimensions on the set of simulated images and comparing critical dimension measurements with the set of model parameters to provide a set of guidance parameters based on comparison of information from the set of simulated images and the set of model parameters. The method may further comprise receiving auxiliary information associated with target parameters including critical dimension uniformity.

Systems and methods of optimal metrology guidance

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Milpitas, CA, United States of America

Wei Fang