A specimen machining device for machining a specimen by irradiating the specimen with an ion beam includes an ion source for irradiating the specimen with the ion beam, a shielding member disposed on the specimen to block the ion beam, a specimen stage for holding the specimen, a camera for photographing the specimen, a coaxial illumination device for irradiating the specimen with illumination light along an optical axis of the camera, and a processing unit for determining whether to terminate the machining based on an image photographed by the camera. The processing unit performs processing for acquiring information indicating a target machined width, processing for acquiring the image, processing for measuring a machined width on the acquired image, and processing for terminating the machining when the measured machined width equals or exceeds the target machined width.

A method for determining an amount of metallic impurities within silicon. The method includes the steps of (a) providing a rodlike silicon sample and a rodlike seed crystal in a zone melting apparatus, (b) zone melting to form a single silicon crystal having a conical end region with a droplike melt forming at the end of the single silicon crystal in a separation step, (c) cooling of the droplike melt to form a solidified silicon drop, (d) partial or complete dissolution of the silicon drop in an acid, and analyzing the solution obtained in step (d) by a trace analysis technique. Wherein the separation step further includes a remelting step for the silicon sample to reduce its diameter, forming a droplike melting zone, and separation of the seed crystal and the silicon sample by moving the seed crystal and the silicon sample apart from one another.

A method for determining an amount of metallic impurities within silicon. The method includes the steps of (a) providing a rodlike silicon sample and a rodlike seed crystal in a zone melting apparatus, (b) zone melting to form a single silicon crystal having a conical end region with a droplike melt forming at the end of the single silicon crystal in a separation step, (c) cooling of the droplike melt to form a solidified silicon drop, (d) partial or complete dissolution of the silicon drop in an acid, and analyzing the solution obtained in step (d) by a trace analysis technique. Wherein the separation step further includes a remelting step for the silicon sample to reduce its diameter, forming a droplike melting zone, and separation of the seed crystal and the silicon sample by moving the seed crystal and the silicon sample apart from one another.

The invention belongs to the technical field of quantitative statistical distribution analysis for micro-structures of metal materials, and relates to a method for automatic quantitative statistical distribution characterization of dendrite structures in a full view field of metal materials. According to the method based on deep learning in the present invention, dendrite structure feature maps are marked and trained to obtain a corresponding object detection model, so as to carry out automatic identification and marking of dendrite structure centers in a full view field; and in combination with an image processing method, feature parameters in the full view field such as morphology, position, number and spacing of all dendrite structures within a large range are obtained quickly, thereby achieving quantitative statistical distribution characterization of dendrite structures in the metal material. The method is accurate, automatic and efficient, involves a large amount of quantitative statistical distribution information, and is statistically more representative as compared with the traditional measurement of feature sizes of dendrite structures in a single view field.

The invention belongs to the technical field of quantitative statistical distribution analysis for micro-structures of metal materials, and relates to a method for automatic quantitative statistical distribution characterization of dendrite structures in a full view field of metal materials. According to the method based on deep learning in the present invention, dendrite structure feature maps are marked and trained to obtain a corresponding object detection model, so as to carry out automatic identification and marking of dendrite structure centers in a full view field; and in combination with an image processing method, feature parameters in the full view field such as morphology, position, number and spacing of all dendrite structures within a large range are obtained quickly, thereby achieving quantitative statistical distribution characterization of dendrite structures in the metal material. The method is accurate, automatic and efficient, involves a large amount of quantitative statistical distribution information, and is statistically more representative as compared with the traditional measurement of feature sizes of dendrite structures in a single view field.

A semiconductor analysis system includes a machining device that machines a semiconductor wafer to prepare a thin film sample for observation, a transmission electron microscope device that acquires a transmission electron microscope image of the thin film sample, and a host control device that controls the machining device and the transmission electron microscope device. The host control device evaluates the thin film sample based on the transmission electron microscope image, updates acquisition conditions of the transmission electron microscope image based on an evaluation result of the thin film sample, and outputs the updated acquisition conditions to the transmission electron microscope device

The invention relates to a method for processing an object using a material processing device that has a particle beam apparatus. The method comprises the following steps: determining a region of interest of the object on or in a first material region of the object, ablating material from a second material region adjoining the first material region by means of an ablation device, recognizing a geometric shape of the first material region, the geometric shape having a center, ablating material from a second portion of the first material region adjoining a first portion by means of a particle beam, the first portion having a first subregion and a second subregion, the region of interest being arranged in the first subregion, recognizing a further geometric shape of the first material region, the further geometric shape having a further center at a second position, relative positioning of the object such that the first position corresponds to the second position, and ablating material from the second subregion by means of the particle beam.

To prepare a sample for atom probe tomography, a raw sample body having a surface and a region of interest (ROI) to be inspected by APT is provided. Pillars containing the ROI are formed into the surface of the raw sample body via ablation of material of the raw sample body from the surface with an ultra-short pulsed laser. Redeposited ablated material is removed in the region of the formed pillars. The surface of the formed pillars is polished. A preparation device to perform such a preparation method includes a sample handling unit, a pillar forming unit including an ultra-short pulsed laser, a removal unit to remove redeposited ablated material, and a polishing unit. The result is an efficient preparation of robust samples for atom probe tomography. To investigate a region of interest of a sample, the preparation method is performed and then atom probe tomography of the region of interest is performed.

To prepare a sample for atom probe tomography, a raw sample body having a surface and a region of interest (ROI) to be inspected by APT is provided. Pillars containing the ROI are formed into the surface of the raw sample body via ablation of material of the raw sample body from the surface with an ultra-short pulsed laser. Redeposited ablated material is removed in the region of the formed pillars. The surface of the formed pillars is polished. A preparation device to perform such a preparation method includes a sample handling unit, a pillar forming unit including an ultra-short pulsed laser, a removal unit to remove redeposited ablated material, and a polishing unit. The result is an efficient preparation of robust samples for atom probe tomography. To investigate a region of interest of a sample, the preparation method is performed and then atom probe tomography of the region of interest is performed.

Systems and methods of sample preparation using dual ion beam trenching are described. In an example, an inside of a semiconductor package is non-destructively imaged to determine a region of interest (ROI). A mask is positioned over the semiconductor package, and a mask window is aligned with the ROI. A first ion beam and a second ion beam are swept, simultaneously or sequentially, along an edge of the mask window to trench the semiconductor package and to expose the ROI for analysis.