Carl Zeiss X-ray Microscopy, Inc.

Carl Zeiss Smt Gmbh

Carl Zeiss Industrielle Messtechnik Gmbh

Christoph Hilmar Graf Vom Hagen

Johannes Ruoff

Matthew Andrew

Lars Omlor

Thomas Matthew Gregorich

Heiko Feldmann

Michael Totzeck

Thomas Anthony Case

Jens Timo Neumann

Marcin B Bauza

Ramani Pichumani

Diana Spengler

Xiaochao Xu

Andriy Andreyev

Claus Hermannstaedter

Markus Birkhold

Hrishikesh Bale

Robert Zarnetta

Juan Atkinson Mora

Gerhard Krampert

A detection system for an x-ray microscopy system utilizes high bandgap, direct conversion x-ray detection materials. The signal of the x-ray projection is recorded in a spatial light modulator such as a liquid crystal (LC) light valve. The light valve is then read-out by a polarized light optical microscope. This configuration will mitigate the loss of light in the optical system over the current scintillator-optical microscope-camera detection systems.

High resolution light valve detector for detecting x-ray

A method and an arrangement for producing a workpiece using additive manufacturing techniques involve pre-process, in-process and post-process measurement in order to determine individual characteristics of one or more workpiece layers. In particular, dimensional and/or geometrical characteristics of a workpiece layer are measured before the next workpiece layer is produced. Advantageously, production parameters are controlled in response to individual material characteristics determined prior to the production process. Also advantageously, measurement results are fed back into a production process in order to increase accuracy, reliability, repeatability and precision of the production process.

Adaptive closed-loop control of additive manufacturing for producing a workpiece

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.

Accessible neural network image processing workflow

A method for acquiring a 3D image of a sample structure includes acquiring a first raw 2D set of 2D images of a sample structure at a limited number of raw sample planes; calculating a 3D image of the sample structure represented by a 3D volumetric image data set; and extracting a measurement parameter from the 3D volumetric image data set. A further number of interleaving 2D image acquisitions are recorded at a further number of interleaved sample planes which do not coincide with previous acquisition sample planes. The steps 'calculating,' “extracting” and “assigning” are repeated for the further interleaving 2D set until convergence or a maximum number of 2D image acquisitions is recorded. A projection system used for such method comprises a projection light source, a rotatable sample structure holder and a spatially resolving detector. Such method can also be used to acquire virtual tomographic images of a sample.

Method to acquire a 3D image of a sample structure

A detection system serves for X-ray inspection of an object. An imaging optical arrangement serves to image the object in an object plane illuminated by X-rays generated by an X-ray source. The imaging optical arrangement comprises an imaging optics to image a transfer field in a field plane into a detection field in a detection plane. A detection array is arranged at the detection field. An object mount holds the object to be imaged and is movable relative to the X-ray source via an object displacement drive along at least one lateral object displacement direction in the object plane. A shield stop with a transmissive shield stop aperture is arranged in an arrangement plane in a light path and is movable via a shield stop displacement drive in the arrangement plane. A control device has a drive control unit, which is in signal connection with the shield stop displacement drive and with the object displacement drive for synchronizing a movement of the shield stop displacement drive and the object displacement drive. The result is an optimization of an X-ray illumination of the object to achieve a high-resolution object imaging.

Detection system for X-ray inspection of an object

An x-ray microscopy method that obtains a classification of different particles by distinguishing between different material phases through a combination of image processing involving morphological edge enhancement and possibly resolved absorption contrast differences between the phases along with optional wavelet filtering.

Edge phase effects removal using wavelet correction and particle classification using combined absorption and phase contrast

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Oakland, CA, United States of America

Christoph Hilmar Graf Vom Hagen