Carl Zeiss Smt Gmbh

Carl Zeiss Industrielle Messtechnik Gmbh

Abhilash Srikantha

Thomas Korb

Christian Wojek

Jens Timo Neumann

Eugen Foca

Ramani Pichumani

Keumsil Lee

Joaquin Correa

Timo Stich

Ecaterina Schuster

A method includes obtaining an image data set that depicts semiconductor components, and applying a hierarchical bricking to the image data set. In this case, the bricking includes a plurality of bricks on a plurality of hierarchical levels. The bricks on different hierarchical levels have different image element sizes of corresponding image elements.

Processing image data sets

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

Methods for determining metrology sites for products includes detecting corresponding objects in measurement data of one or more product samples, and aligning the detected objects are aligned. The methods also include analyzing the aligned objects, and determining metrology sites based on the analysis. Devices use such methods to determine metrology sites for products.

Method and devices for determining metrology sites

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Neu-Ulm, Germany

Abhilash Srikantha