Asml Netherlands B.v.

Cyrus Emil Tabery

Chenxi Lin

Youping Zhang

Yi Zou

Hakki Ergün Cekli

Yana Cheng

Simon Hendrik Celine Van Gorp

Simon Philip Spencer Hastings

Boris Menchtchikov

Maxime Philippe Frederic Genin

Alexander Ypma

Peter Ten Berge

Michael James Lercel

Ruben Alvarez Sanchez

Dag Sonntag

Pierre-Yves Jerome Yvan Guittet

Christiaan Theodoor De Ruiter

Shih-Chin Liu

Wei Duan

Wim Tjibbo Tel

Daan Maurits Slotboom

Abraham Slachter

Brennan Peterson

Davit Harutyunyan

Tzu-Chao Chen

Matthijs Cox

Vadim Yourievich Timoshkov

Koen Wilhelmus Cornelis Adrianus Van Der Straten

Nicolaas Petrus Marcus Brantjes

Maxim Philippe Frederic Genin

A measurement mark is disclosed. According to certain embodiments, the measurement mark includes a set of first test structures developed in a first layer on a substrate, each of the set of first test structures comprising a plurality of first features made of first conducting material. The measurement mark also includes a set of second test structures developed in a second layer adjacent to the first layer, each of the set of second test structures comprising a plurality of second features made of second conducting material. The measurement mark is configured to indicate connectivity between the set of first test structures and associated second test structures in the set of second test structures when imaged using a voltage-contrast imaging method.

Voltage contrast metrology mark

A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.

Method to predict yield of a device manufacturing process

A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.

Method of manufacturing devices

A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

Method for controlling a manufacturing process and associated apparatuses

A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.

Method for determining root cause affecting yield in a semiconductor manufacturing process

A method and associated computer program for predicting an electrical characteristic of a substrate subject to a process. The method includes determining a sensitivity of the electrical characteristic to a process characteristic, based on analysis of electrical metrology data including electrical characteristic measurements from previously processed substrates and of process metrology data including measurements of at least one parameter related to the process characteristic measured from the previously processed substrates; obtaining process metrology data related to the substrate describing the at least one parameter; and predicting the electrical characteristic of the substrate based on the sensitivity and the process metrology data.

A method for determining a control parameter for an apparatus used in a semiconductor manufacturing process, the method including: obtaining performance data associated with a substrate subject to the semiconductor manufacturing process; obtaining die specification data including values of an expected yield of one or more dies on the substrate based on the performance data and/or a specification for the performance data; and determining the control parameter in dependence on the performance data and the die specification data. Advantageously, the efficiency and/or accuracy of processes is improved by determining how to perform the processes in dependence on dies within specification.

Method for determining a control parameter for an apparatus utilized in a semiconductor manufacturing process

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San Jose, CA, United States of America

Cyrus Emil Tabery