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1. 发明申请

WO2023072557A1 METHOD TO ACQUIRE A 3D IMAGE OF A SAMPLE STRUCTURE 审中-公开
公开(公告)号：WO2023072557A1
公开(公告)日：2023-05-04
申请号：PCT/EP2022/077969
申请日：2022-10-07
申请人： CARL ZEISS SMT GMBH , CARL ZEISS X-RAY MICROSCOPY INC.
发明人： PICHUMANI, Ramani , GRAF VOM HAGEN, Christoph Hilmar , NEUMANN, Jens Timo , RUOFF, Johannes , GREGORICH, Thomas Matthew
IPC分类号： G01N23/046 , G06T11/00
摘要： In a method to acquire a 3D image of a sample structure initially a first raw 2D set of 2D images of a sample structure is acquired at a limited number of raw sample planes. From this first raw 2D set a 3D image of the sample structure being represented by a 3D volumetric image data set is calculated and a measurement parameter is extracted from the 3D volumetric image data set. Such measurement parameter is assigned to the number of 2D image acquisitions recorded during the acquisition step. Then, a further interleaving 2D set of 2D images of the sample structure is required by recording a further number of interleaving 2D image acquisitions at a further number of interleaved sample planes which do not coincide with the previous acquisition sample planes. The steps "calculating," "extracting" and "assigning" are repeated for the further interleaving 2D set. The actual and the last extracted measurement parameters are compared to check whether a convergence criterion is met. If not, the steps "acquiring," "calculating," "extracting," "assigning" and "comparing" are repeated for a further interleaving 2D set including a further number of interleaving 2D image acquisitions at a further number of interleaved sample planes which do not coincide with the previous acquisition sample planes. This is done until the convergence criterion is met or until a given maximum number of 2D image acquisitions is recorded. The measurement parameter and a total number of recorded 2D image acquisitions are output. A projection system used for such method comprises a projection light source, a rotatable sample structure holder and a spatially resolving detector. Alternatively or in addition, such method can be used by a data processing system to acquire virtual tomographic images of a sample. With such method, a sample throughput is improved.

2. 发明申请

WO2023020782A1 IMAGING OPTICAL ARRANGEMENT TO IMAGE AN OBJECT ILLUMINATED BY X-RAYS 审中-公开
公开(公告)号：WO2023020782A1
公开(公告)日：2023-02-23
申请号：PCT/EP2022/070678
申请日：2022-07-22
申请人： CARL ZEISS SMT GMBH , CARL ZEISS X-RAY MICROSCOPY, INC.
发明人： RUOFF, Johannes , ATKINSON MORA, Juan , CASE, Thomas Anthony , FELDMANN, Heiko , GRAF VOM HAGEN, Christoph Hilmar , GREGORICH, Thomas Matthew , KRAMPERT, Gerhard
IPC分类号： G21K7/00 , H01J37/244
摘要： An imaging optical arrangement serves to image an object illuminated by X-rays. An imaging optics serves to image a transfer field in a field plane into a detection field in a detection plane. A layer of scintillator material is arranged at the transfer field. A stop is arranged in a pupil plane of the imaging optics. The imaging optics has an optical axis. A center of a stop opening of the stop is arranged at a decentering distance with respect to the optical axis. Such imaging optical arrangement ensures a high quality imaging of the object irrespective of a tilt of X-rays entering the transfer field. The imaging optical arrangement is part of a detection assembly further comprising a detection array and an object mount. Such detection assembly is part of a detection system further comprising a X-ray source.

3. 发明申请

WO2023283402A1 ACCESSIBLE NEURAL NETWORK IMAGE PROCESSING WORKFLOW 审中-公开
公开(公告)号：WO2023283402A1
公开(公告)日：2023-01-12
申请号：PCT/US2022/036452
申请日：2022-07-08
申请人： CARL ZEISS X-RAY MICROSCOPY, INC. , ANDREW, Matthew , OMLOR, Lars , ANDREYEV, Andriy , GRAF VOM HAGEN, Christoph Hilmar
发明人： ANDREW, Matthew , OMLOR, Lars , ANDREYEV, Andriy , GRAF VOM HAGEN, Christoph Hilmar
IPC分类号： G06T11/00
摘要： Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data – optionally collected at a higher throughput - to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

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