Abstract:
A focal position estimation system is a system for estimating a focal position when in focus corresponding to an estimation target image, and includes: an estimation target image acquisition unit that acquires an estimation target image; and a focal position estimation unit that outputs a feature quantity of the estimation target image from the estimation target image by using a feature quantity output model and estimates a focal position when in focus corresponding to the estimation target image from the output feature quantity, wherein the feature quantity output model is generated by machine learning from a plurality of learning images associated with focal position information related to a focal position at the time of imaging, and feature quantities of two different learning images are compared with each other according to focal position information associated with the two different learning images, and machine learning is performed based on the comparison result.
Abstract:
A focal position estimation method is a method of estimating a focal position when in focus corresponding to an estimation target image, and includes: an estimation target image acquisition step for acquiring an estimation target image; and a focal position estimation step for estimating a focal position when in focus corresponding to the estimation target image and according to a position in the estimation target image, from the estimation target image acquired in the estimation target image acquisition step, by using a focal position estimation model that is generated through machine learning training and that receives information based on an image as its input and outputs information indicating a focal position when in focus according to a position in the image.
Abstract:
An autofocus support method according to an embodiment supports autofocus for a semiconductor device having a substrate and a device pattern formed on one main surface side of the substrate. The method includes: a step of acquiring a first image focused on the substrate; a step of acquiring a spatial frequency image from the first image by Fourier transform and generating mask data for masking linear patterns in the same direction on the substrate based on the spatial frequency image; a step of performing filtering on a plurality of second images, which are captured by using an imaging device while changing the focal position of the imaging device on the other main surface side of the substrate, by using the mask data; and a step of focusing the imaging device on the device pattern based on the second image after filtering.
Abstract:
A semiconductor inspection method by an observation system includes a step of acquiring a first pattern image showing a pattern of a semiconductor device, a step of acquiring a second pattern image showing a pattern of the semiconductor device and having a different resolution from a resolution of the first pattern image, a step of learning a reconstruction process of the second pattern image using the first pattern image as training data by machine learning, and reconstructing the second pattern image into a reconstructed image having a different resolution from a resolution of the second pattern image by the reconstruction process based on a result of the learning, and a step of performing alignment based on a region calculated to have a high degree of certainty by the reconstruction process in the reconstructed image and the first pattern image.
Abstract:
The image processing device 10 includes a template preparation unit 15 for preparing, from a template included in pixels of M rows and M columns (M is an integer not less than 3) corresponding to a molecular model, a partial template corresponding to a shape for which a shape of the molecular model is divided, an evaluation value calculation unit 17 for evaluating, in the optical image, by use of the partial template, matching between the optical image and the partial template to calculate an evaluation value for every plurality of the attention pixels, and a molecular location identification unit 18 for identifying the molecular location in the optical image based on the evaluation value.
Abstract:
A feature quantity output model generation system is a system for generating a feature quantity output model to which information based on an image is input and which outputs a feature quantity of the image, and includes: a learning image acquisition unit that acquires a plurality of learning images associated with focal position information related to a focal position at the time of imaging; and a feature quantity output model generation unit that generates a feature quantity output model by machine learning from the acquired learning images, wherein the feature quantity output model generation unit compares the feature quantities of two different learning images according to focal position information associated with the two different learning images and performs machine learning based on the comparison result.
Abstract:
An observation system includes a detector that detects light from a semiconductor device and outputs a detection signal, a 2D camera, an optical device that guides light to the detector and the 2D camera, an image processing unit that generates a first optical image of the semiconductor device based on the detection signal and receives an input of a first CAD image, an image analysis unit that learns a conversion process of the first CAD image by machine learning using the first optical image as training data, and converts the first CAD image into a second CAD image resembling the first optical image by the conversion process based on a result of the learning, and an alignment unit that performs alignment based on a second optical image and the second CAD image.
Abstract:
A semiconductor apparatus examination method includes a step of acquiring a first interference waveform based on signals from a plurality of drive elements according to light from a first light beam spot including the plurality of drive elements in a semiconductor apparatus, a step of acquiring a second interference waveform based on signals from the plurality of drive elements according to light from a second light beam spot having a region configured to partially overlap the first spot and including the plurality of drive elements, and a step of separating a waveform signal for each of the drive elements in the first and second spots based on the first and second interference waveforms.
Abstract:
An inclination estimation system is a system for estimating the inclination of an imaging target captured in an image, and includes: an estimation target image acquisition unit for acquiring estimation target images from the image; a focal position estimation unit for outputting feature quantities from estimation target images by using a feature quantity output model and estimating focal positions when in focus corresponding to the estimation target images; and an inclination estimation unit for estimating the inclination of the imaging target from the focal positions when in focus, wherein the feature quantity output model is generated by machine learning from a learning images associated with focal position information, and feature quantities of two different learning images are compared with each other according to focal position information associated with the two different learning images, and machine learning is performed based on a result of the comparison.
Abstract:
A semiconductor device examination method includes a step of acquiring a first interference waveform based on signals from a plurality of drive elements according to light from a first light beam spot including the plurality of drive elements in a semiconductor device, a step of acquiring a second interference waveform based on signals from the plurality of drive elements according to light from a second light beam spot having a region configured to partially overlap the first spot and including the plurality of drive elements, and a step of separating a waveform signal for each of the drive elements in the first and second spots based on the first and second interference waveforms.