Hitachi High-tech Corporation

Hitachi-high-technologies Corporation

Naoaki Kondo

Minoru Harada

Yuji Takagi

Takehiro Hirai

Yohei Minekawa

Hideki Nakayama

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

A sample observation device images a sample placed on a movable table by irradiating and scanning the sample with a charged particle beam of a microscope. A degraded image having poor image quality and a high quality image having satisfactory image quality which are acquired at the same location of the sample by causing the charged particle microscope to change an imaging condition for imaging the sample are stored. An estimation process parameter is calculated for estimating the high quality image from the degraded image by using the stored degraded image and high quality image. A high quality image estimation unit processes the degraded image obtained by causing the charged particle microscope to image the desired site of the sample by using the calculated estimation process parameter. Thereby, the high quality image obtained at the desired site is estimated, and then the estimated high quality image is output.

Sample observation device and sample observation method

A defect observation device comprising: a defect determination coordinate creation unit by which the coordinates of a plurality of second defect candidates are determined as overlapping defect candidate coordinates, the plurality of second defect candidates respectively having, in a plurality of second imaging visual field regions overlapping a first imaging visual field region, a circuit pattern which partly overlaps a circuit pattern in the first imaging visual field region, in which a first defect candidate for defect determination among a plurality of defect candidates of a sample is present; a pseudo-reference image generation unit which generates a pseudo-reference image including a circuit pattern of the first defect candidate by superimposing a plurality of images respectively captured at the plurality of overlapping defect candidate coordinates; and a defect determination unit which compares an image for defect determination captured at the coordinates of the first defect candidate with the pseudo-reference image to determine the presence or absence of a defect in the image for defect determination.

Defect observation device and defect observation method

A versatile template generation unit cuts a first region in which a similarity level to a template image is a first similarity level and a second region in which the similarity level to the template image is a second similarity level different from the first similarity level, from an input image including an alignment mark, to generate a versatile template image.

Wafer 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

Provided is a defect classification apparatus classifying images of defects of a sample included in images obtained by capturing the sample, the apparatus including an image storage unit for storing the images of the sample acquired by an external image acquisition unit, a defect class storage unit for storing types of defects included in the images of the sample, an image processing unit for extracting images of defects from the images from the sample, processing the extracted images of defects and generating a plurality of defect images, a classifier learning unit for learning a defect classifier using the images of defects of the sample extracted by the image processing unit and data of the plurality of generated defect images, and a defect classification unit for processing the images of the sample by using the classifier learned by the classifier learning unit, to classify the images of defects of the sample.

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Tokyo, Japan

Naoaki Kondo