Hitachi-high-technologies Corporation

Hitachi High-tech Corporation

Yohei Minekawa

Takehiro Hirai

Minoru Harada

Yuji Takagi

Naoaki Kondo

Ryo Nakagaki

Yuko Otani

Kenji Nakahira

Yuya Isomae

Makoto Ono

Muneyuki Fukuda

Kazuo Aoki

Akira Ito

Junko Konishi

Yutaka Tandai

Nobuhiko Kanzaki

Katsuhiro Kitahashi

Miyuki Fukuda

Takashi Nobuhara

Takanori Kishimoto

Hideki Nakayama

Kohei Chiba

Kaori Yaeshima

Yuki Doi

The invention provides a sample observation system including a scanning electron microscope and a calculator. The calculator: (1) acquires a plurality of images captured by the scanning electron microscope; (2) acquires, from the plurality of images, a learning defect image including a defect portion and a learning reference image not including the defect portion; (3) calculates estimation processing parameters by using the learning defect image and the learning reference image; (4) acquires an inspection defect image including a defect portion; and (5) estimates a pseudo reference image by using the estimation processing parameters and the inspection defect image.

Sample observation system and image processing method

In learning processing performed before sample observation processing (steps Sto S), the sample observation device acquires a low-picture quality learning image under a first imaging condition for each defect position indicated by defect position information, determines an imaging count of a plurality of high-picture quality learning images associated with the low-picture quality learning image for each defect position and a plurality of imaging points based on a set value of the imaging count, acquires the plurality of high-picture quality learning images under a second imaging condition (step S), learns a high-picture quality image estimation model using the low-picture quality learning image and the plurality of high-picture quality learning images (step S), and adjusts a parameter related to the defect detection in the sample observation processing using the high-picture quality image estimation model (step S).

Sample observation device and method

A defect inspecting system includes a detector configured to image a sample and a host control device that acquires an inspection image including a defect and a plurality of reference images not including a defect site and generates a pseudo defect image by editing a predetermined reference image among the plurality of acquired reference images. An initial parameter is determined with which the pseudo defect site is detectable from the pseudo defect image. The host control device acquires a defect candidate site from the inspection image using the initial parameter, estimates a high-quality image from an image of a site corresponding to the defect candidate site using the parameter acquired in image quality enhancement, and specifies an actual defect site in the inspection image by executing defect discrimination. A parameter is determined with which a site close to the specified actual defect site is detectable using the inspection image.

Defect inspecting system and defect inspecting method

Provided is a wafer observation apparatus includes: a scanning electron microscope; a control unit which includes a wafer observation unit that observes a wafer of a semiconductor device, and an image acquisition unit that acquires a wafer image; a storage unit which includes an image storage unit that stores the wafer image and a template image, and a recipe storage unit that stores a wafer alignment recipe including a matching success and failure determination threshold value, an image processing parameter set, and a use priority associated with the template image; and a calculation unit which includes a recipe reading unit that reads the template image and the wafer alignment recipe, a recipe update necessity determination unit that determines update necessity of the wafer alignment recipe, and a recipe updating unit that updates the wafer alignment recipe based on a determination result in the recipe update necessity determination unit.

Wafer observation apparatus and wafer observation method

Provided is a technique capable of achieving both throughput and robustness for a function of adjusting brightness (B) and contrast (C) of a captured image in a charged particle beam device. The charged particle beam device includes a computer system having a function (ABCC function) of adjusting the B and the C of an image obtained by imaging a sample. The computer system determines whether adjustment is necessary based on a result obtained by evaluating a first image obtained by imaging an imaging target of the sample (step S), executes, when the adjustment is necessary based on a result of the determination, the adjustment on a second image of the imaging target to set an adjusted B value and an adjusted C value (step S), and captures a third image of the imaging target based on the adjusted setting values to generate an image for observation (step S).

Charged particle beam device

A defect observation device is configured to include a charged particle microscope and a controller including a control unit that controls the charged particle microscope, a storage unit, and an arithmetic unit, in which the control unit controls the charged particle microscope under a first condition to acquire a first image of an observation target region of the sample, the arithmetic unit extracts first position information of the observation target region from the obtained first image, the control unit controls the charged particle microscope under a second condition to acquire a second image of the observation target region of the sample, and the arithmetic unit performs an image quality conversion process to match the image quality of the acquired second image with the image quality of the first image using the image quality conversion process parameters to process the second image subjected to the image quality conversion process.

Defect observation device

In a device for observing a semiconductor wafer, a positional relationship between an in-wafer region and a background region in an imaging field of view is not constant when an outer peripheral portion of the wafer is imaged, which results in an increase in the quantity of calculation in defect detection and image classification processing and makes it difficult to efficiently perform defect observation and analysis. There is provided a defect observation system for a semiconductor wafer, and the system includes: a stage on which the semiconductor wafer is placed and which is movable in an XY direction, an imaging unit that is configured to image a portion including an edge of the semiconductor wafer, and an image output unit that is configured to, with respect to a plurality of images obtained by imaging, output images in which edges of the wafer are substantially in parallel among the plurality of images.

Defect observation system and defect observation method for semiconductor wafer

A defect observation device detects a defect with high accuracy regardless of a defect size. One imaging configuration for observing an observation target on a sample is selected from an optical microscope, an optical microscope, and an electron microscope, and an imaging condition of the selected imaging configuration is controlled.

A defect observation apparatus includes a storage unit configured to store defect information about defects detected by an external inspection apparatus; a first imaging unit configured to capture an image of a defect using a first imaging condition and a second imaging condition; a control unit configured to correct positional information on the defect using the image captured with the first imaging unit; and a second imaging unit configured to capture an image of the defect based on the corrected positional information.

Defect observation apparatus

To review minute defects that were buried in roughness scattered light with an observation device provided with a dark-field microscope, a scanning electron microscope (SEM), and a control unit, the present invention configures the dark-field microscope by installing a filter for blocking a portion of the scattered light, an imaging lens for focusing the scattered light that has passed through the filter, and a detector for dividing the image of the scattered light focused by the imaging lens into the polarization directions converted by a wavelength plate and detecting the resulting images, and the control has a calculation unit for determining the position of a defect candidate detected by another inspection device using the plurality of images separated into polarization directions and detected by the detector.

Defect reviewing method and device

To quantify the degree of a defect, and provide information useful for yield management. Disclosed is a defect quantification method wherein: a defect image is classified; a measurement region and a measurement area are set to each of the defect image and a reference image on the basis of defect image classification results, said reference image corresponding to the defect image; and an evaluation value of a defect is calculated using each of the measurement values obtained from each of the measurement areas of the defect image and the reference image, and the defect is quantified.

Defect quantification method, defect quantification device, and defect evaluation value display device

Provided is a GUI including: an unadded pane region that hierarchically displays folders which are sets of images having no class information added thereto; an image pane region that displays the images displayed in the unadded pane region, the displayed images having no classification added thereto; and a class pane region that displays images having classification added thereto, wherein by externally inputting class information for one image having the class information added thereto, the input class information is displayed.

Defect classification apparatus and defect classification method

A defect image classification apparatus includes a control unit that selects images obtained from at least some detectors among a plurality of detectors, associated with kinds of defects to be a classification result of an automatic defect classification processing unit, as images displayed initially on a display unit. The control unit associates the kinds of the defects and the images displayed initially on the display unit, on the basis of a switching operation log when a user classifies images of defects determined previously as the same kinds as the kinds of the defects determined by the automatic defect classification processing unit.

Defect image classification apparatus

In automatic defect classification, a classification recipe must be set for each defect observation device. If a plurality of devices operate at the same stage, the classification class in the classification recipes must be the same. Problems have arisen whereby differences occur in the classification class in different devices when a new classification recipe is created. This defect classification system has a classification recipe storage unit; an information specification unit, the stage of a stored image, and device information. A corresponding defect specification unit specifies images of the same type of defect from images obtained from different image pickup devices at the same stage. An image conversion unit converts the images obtained from the different image pickup devices at the same stage into comparable similar images; and a recipe update unit records the classification classes in the classification recipes corresponding to the specified images of the same type of defect.

Defect classification method, and defect classification system

A method for reviewing defect, comprising the steps of: as an image acquisition step, imaging a surface of a sample using arbitrary image acquisition condition selected from a plurality of image acquisition conditions and obtaining a defect image; as a defect position calculation step, proceeding the defect image obtained by the image acquisition step and calculating a defect position on the surface of the sample; as a defect detection accuracy calculation step, obtaining a defect detection accuracy of the defect position calculated by the defect position calculation step; and as a conclusion determination step, determinating whether the defect detection accuracy obtained by the defect detection accuracy calculation step meets a predetermined requirement or not; wherein until it is determined that the defect detection accuracy obtained by the defect detection accuracy calculation step meets a predetermined in the conclusion determination step, the image acquisition condition is selected from the plurality of image acquisition conditions once again and the image acquisition step, the defect position calculation step, the defect detection accuracy calculation step and the conclusion determination step are repeated.

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Tokyo, Japan

Yohei Minekawa