Metrology Method and Apparatus, Computer Program and Lithographic System

Patent metadata

Field	Value
Publication number	US-2016325504-A1
Application number	US-201615138903-A
Country	US
Kind code	A1
Filing date	Apr 26, 2016
Priority date	May 7, 2015
Publication date	Nov 10, 2016
Grant date	—

Abstract

Disclosed are a method, computer program and associated apparatuses for metrology. The method includes acquiring inspection data comprising a plurality of inspection data elements, each inspection data element having been obtained by inspection of a corresponding target structure formed using a lithographic process; and performing an unsupervised cluster analysis on said inspection data, thereby partitioning said inspection data into a plurality of clusters in accordance with a metric. In an embodiment, a cluster representative can be identified for each cluster. The cluster representative may be reconstructed and the reconstruction used to approximate the other members of the cluster.

First claim

1 . A method of metrology comprising:
acquiring inspection data, said inspection data comprising a plurality of inspection data elements, each inspection data element having been obtained by inspection of a corresponding target structure formed using a lithographic process; and performing an unsupervised cluster analysis on said inspection data, thereby partitioning said inspection data into a plurality of clusters in accordance with a metric.

2 . (canceled)

3 . The method as claimed in claim 2 , wherein said spectra or images comprise diffraction spectra or images obtained by said inspection using a scatterometer.

4 . The method as claimed in claim 1 , wherein said inspection data comprises values for at least one parameter of said corresponding target structure as determined by a reconstruction operation on said inspection data.

5 . The method as claimed in claim 1 , wherein said partitioning of said inspection data is performed in accordance with a relative distance, as defined by the metric, between each inspection data element and a cluster center of each cluster.

6 . (canceled)

7 . The method as claimed in claim 1 , wherein said partitioning of said inspection data is performed in accordance with one or more statistical distribution models, such that each cluster is defined as comprising the inspection data elements belonging to the same distribution.

8 .- 9 . (canceled)

10 . The method as claimed in claim 1 , comprising the step of determining a representative inspection data element for each of said clusters, each representative inspection data element corresponding to a representative target structure.

11 . The method as claimed in claim 10 , wherein each representative inspection data element is the closest to an average inspection data element, in terms of the metric, for its corresponding cluster.

12 . (canceled)

13 . The method as claimed in claim 10 , comprising performing a reconstruction to obtain at least one reconstructed parameter value from each representative data element, said at least one reconstructed parameter value comprising a value for at least one parameter of the corresponding representative target structure.

14 . The method as claimed in claim 13 , wherein the at least one reconstructed parameter values for each representative target structure are used to derive a nominal value for at least one model parameter in subsequent reconstructions.

15 . The A method as claimed in claim 13 , comprising performing an approximated reconstruction of each target structure, other than the representative target structures, based upon the at least one reconstructed parameter value of at least one of the representative target structures.

16 . (canceled)

17 . The method as claimed in claim 15 , wherein the approximated reconstruction for each target is based upon the at least one reconstructed parameter value for the representative target structure of the cluster of which that target is, or is most likely to be, a member.

18 . The method as claimed in claim 15 , wherein the approximated reconstruction for each target is based upon a weighted average of reconstructed parameter values for all, or a subset of the representative target structures.

19 .- 22 . (canceled)

23 . The method as claimed in claim 1 , wherein the number of clusters is automatically learned.

24 . The method as claimed in claim 23 , wherein the number of clusters is automatically learned by performing said clustering analysis multiple times, each time partitioning said inspection data into n clusters, wherein n is increased each time a cluster analysis is performed, determining an average within cluster distance according to the metric for each cluster analysis, and selecting the cluster analysis for which the average within cluster distance meets a threshold criterion.

25 .- 27 . (canceled)

28 . The method as claimed in claim 1 , wherein the step of acquiring inspection data comprises inspecting said target structures by illuminating each of said target structures with measurement radiation and detecting the radiation scattered by each target structure.

29 . A metrology apparatus being operable to perform the method comprising:
acquiring inspection data, said inspection data comprising a plurality of inspection data elements, each inspection data element having been obtained by inspection of a corresponding target structure formed using a lithographic process; and performing an unsupervised cluster analysis on said inspection data, thereby partitioning said inspection data into a plurality of clusters in accordance with a metric.

30 . The metrology apparatus as claimed in claim 29 , comprising:
a support for a substrate having a plurality of target structures thereon; an optical system for measuring each target structure; and a processor arranged to at least to perform said step of performing a cluster analysis.

31 . A lithographic system comprising:
a lithographic apparatus comprising: an illumination optical system arranged to illuminate a pattern; a projection optical system arranged to project an inspection of the pattern onto a substrate; and a metrology apparatus comprising:
acquiring inspection data, said inspection data comprising a plurality of inspection data elements, each inspection data element having been obtained by inspection of a corresponding target structure formed using a lithographic process; and
performing an unsupervised cluster analysis on said inspection data, thereby partitioning said inspection data into a plurality of clusters in accordance with a metric.

32 . A computer program comprising processor readable instructions which, when run on suitable processor controlled apparatus, cause the processor controlled apparatus to perform the method comprising:
acquiring inspection data, said inspection data comprising a plurality of inspection data elements, each inspection data element having been obtained by inspection of a corresponding target structure formed using a lithographic process; and performing an unsupervised cluster analysis on said inspection data, thereby partitioning said inspection data into a plurality of clusters in accordance with a metric.

33 . A computer program carrier comprising a computer program comprising a processor readable instructions which, when run on suitable processor controlled apparatus, cause the processor controlled apparatus to perform the method comprising:
acquiring inspection data, said inspection data comprising a plurality of inspection data elements, each inspection data element having been obtained by inspection of a corresponding target structure formed using a lithographic process; and performing an unsupervised cluster analysis on said inspection data, thereby partitioning said inspection data into a plurality of clusters in accordance with a metric.

Assignees

• Asml Netherlands Bv

Inventors

• Mossavat Seyed Iman
• Van Leest Adriaan Johan

Classifications

• G05B19/4099

  Surface or curve machining, making three-dimensional [3D] objects, e.g. desktop manufacturing · CPC title

• B29C64/386 Primary

  Data acquisition or data processing for additive manufacturing · CPC title

• G05B2219/49007

  Making, forming 3-D object, model, surface · CPC title

• G03F7/70625

  Dimensions, e.g. line width, critical dimension [CD], profile, sidewall angle or edge roughness · CPC title

• B33Y50/02

  for controlling or regulating additive manufacturing processes · CPC title