Carl Zeiss Smt Gmbh

Cadence Design Systems, Inc.

Carl Zeiss X-ray Microscopy, Inc.

Ramani Pichumani

Jens Timo Neumann

Thomas Korb

Donald John O'Riordan

Johannes Ruoff

Eugen Foca

Christian Wojek

Dmitry Klochkov

Jonathan Lee Sanders

Christoph Hilmar Graf Vom Hagen

Abhilash Srikantha

Joaquin Correa

Thomas Matthew Gregorich

Catherine Bunting

Heyjin Chris Park

Anthony Morin

David Dryden

A micro-tooling device, such as, for example, a scanning electron microscope or a focused-ion beam microscope, provides images. A first machine-learning algorithm and a second machine-learning algorithm are sequentially coupled. The first machine-learning algorithm determines a progress along a predefined workflow based on feature recognition in images associated with the workflow. The second machine-learning algorithm predicts settings of operational parameters of the micro-tooling device in accordance with the progress along the predefined workflow.

Automated operational control of micro-tooling devices

Methods and evaluation devices for evaluating 3D data of a device under inspection are provided. A first machine learning logic detects target objects, and a second machine learning logic provides a voxel segmentation for the target objects. Based on the segmented voxels, a transformation to feature space is performed to obtain measurement results.

Methods and evaluation devices for analyzing three-dimensional data sets representing devices

A method includes obtaining at least one 2-D image dataset of semiconductor structures formed on a wafer including one or more defects during a wafer run of a wafer using a predefined fabrication process. The method also includes determining, based on at least one machine-learning algorithm trained on prior knowledge of the fabrication process and based on the at least one 2-D image dataset, one or more process deviations of the wafer run from the predefined fabrication process as a root cause of the one or more defects. A 3-D image dataset may be determined as a hidden variable.

Automated root cause analysis for defect detection during fabrication processes of semiconductor structures

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 method to produce a reverse skip list data structure in a computer readable medium, comprising: inputting streamed data to packets created in a temporary memory so as to create a sequence of packets; upon completion of creation of a packet in the stream, transferring the completed packet from the temporary memory to persistent memory; providing each of a plurality of respective packets with a respective pointer that skips over at least one other packet in the packet sequence and that indicates a location in persistent memory of a different respective packet in the packet sequence that was transferred to persistent memory prior to such providing of the respective pointer.

Management of very large streaming data sets for efficient writes and reads to and from persistent storage

A method of processing signals includes: sampling multiple signals, where each sampled signal includes multiple signal values and corresponding time values; partitioning the sampled signals into multiple partitions, where each partition includes signal values and corresponding time values for signals having identical time values within a partition time interval and where at least one additional partition is formed when two sampled signals diverge from identical time values; and saving signal values and time values from partitions in buffers corresponding to the partitions, where the buffers represent allocations of memory for saving partition values.

Scalable storage and retrieval of multiple asynchronous signals

Displaying a large data signal includes processing the input data to generate sets of hierarchical multi-resolution histograms that capture the frequency distribution of the signal, in a single pass. Further, the generation of the sets of hierarchical multi-resolution histograms occurs irrespective of an ordinate range of the signal. Further included is generating display vectors, wherein the sets of hierarchical multi-resolution histograms are used to derive the display vectors when a pre-defined criterion is satisfied.

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Palo Alto, CA, United States of America

Ramani Pichumani