Methods of metrology and associated devices

Patent metadata

Field	Value
Publication number	US-2025199419-A1
Application number	US-202318847453-A
Country	US
Kind code	A1
Filing date	Mar 14, 2023
Priority date	Apr 14, 2022
Publication date	Jun 19, 2025
Grant date	—

Abstract

A method of inferring second metrology data relating to patterned substrate on which patterns have been exposed and on which processing has been performed, from first metrology data measured on the patterned substrate prior to performance of the processing. The method includes obtaining a model including a first model component. The first model component includes a machine learning model component having been trained to map the first metrology data to the second metrology data, the first model component further including a physics-based input channel for receiving physics-based input data. Second metrology data is inferred from the first metrology data using the first model component as biased by the physics-based input data on the physics-based input channel.

First claim

1 . A method of inferring second metrology data relating to at least one patterned substrate on which patterns have been exposed and on which at least one processing step has been performed, from first metrology data measured on the at least one patterned substrate prior to performance of the at least one processing step, the method comprising:
obtaining a model comprising at least one first model component, the at least one first model component comprising a machine learning model component having been trained to map the first metrology data to the second metrology data, the at least one first model component further comprising a physics-based input channel for receiving physics-based input data; and inferring second metrology data from the first metrology data using the at least one first model component as biased by the physics-based input data on the physics-based input channel.

2 . The method as claimed in claim 1 , wherein the first metrology data comprises one or more metrology images or representations thereof.

3 . The method as claimed in claim 2 , wherein the one or more metrology images may include one or more selected from: one or more image based overlay images, one or more scatterometry images captured at a Fourier plane, one or more scatterometry images captured at an image plane, or one or more scanning electron microscope images.

4 . The method as claimed in claim 1 , wherein the second metrology data comprises values for one or more parameters of interest or a description of a spatial variation of one or more parameters of interest.

5 . The method as claimed in claim 4 , wherein the one or more parameters of interest comprise overlay, critical dimension (CD) or edge placement error (EPE).

6 . The method as claimed in claim 4 , wherein the one or more parameters of interest comprise one or more selected from: line-edge roughness, line-width roughness, local critical dimension uniformity, or mean critical dimension.

7 . The method as claimed in claim 1 , wherein the second metrology data comprises e-test data.

8 . The method as claimed in claim 1 , wherein the second metrology data comprises metrology images or representations thereof.

9 . The method as claimed in claim 1 , wherein the physics-based input data comprises one or more processing step parameters related to the at least one processing step and/or one or more patterning parameters relating to the patterns.

10 . The method as claimed in claim 9 , wherein the physics-based input data comprises the one or more patterning parameters relating to the patterns and the one or more patterning parameters comprise at least one target design parameter.

11 . The method as claimed in claim 10 , wherein the at least one target design parameter comprises one or more selected from: target pitch(es), target critical dimension, target design, a target sub-segmentation parameter, target placement in the die, target location, or any parameter of surrounding and/or neighboring structures which surround and/or neighbor the target.

12 . The method as claimed in claim 9 , wherein the physics-based input data comprises the one or more patterning parameters relating to the patterns and the one or more patterning parameters comprise one or more pattern density parameters relating to any of the patterns.

13 . The method as claimed in claim 9 , wherein the at least one processing step comprises at least an etch step; and wherein the physics-based input data comprises the one or more processing step parameters related to the at least one processing step and the one or more processing step parameters comprise one or more etch parameters.

14 . The method as claimed in claim 1 , wherein the model comprises at least one second model component, each at least one second model component comprising a respective physics-based model operable to generate the physics-based input data for the physics-based input channel.

15 . A non-transitory computer program product comprising program instructions configured operable to perform at least the method of claim 1 , when run on a suitable apparatus.

16 . A method of inferring second metrology data relating to at least one patterned substrate on which patterns have been exposed and on which at least one processing step has been performed, from first metrology data measured on the at least one patterned substrate prior to performance of the at least one processing step; the method comprising:
obtaining a model comprising at least one first model component and at least one second model component, wherein the at least one first model component comprises a machine learning model component having been trained to map the first metrology data to the second metrology data and the at least one second model component comprises a physics-based model operable to model an effect of the at least one processing step on the second metrology data; and inferring second metrology data from the first metrology data using the first model component, wherein an output of the at least one second model component is used by the first model component in the inferring the second metrology data.

17 . The method as claimed in claim 16 , wherein the first metrology data comprises metrology images or representations thereof.

18 . The method as claimed in claim 16 , wherein the second metrology data comprises values for one or more parameters of interest or a description of a spatial variation of one or more parameters of interest.

19 . The method as claimed in claim 16 , wherein the second metrology data comprises e-test data or comprises metrology images or representations thereof.

20 . A non-transitory computer program product comprising program instructions configured to perform at least the method of claim 16 , when run on a suitable apparatus.

Assignees

• Asml Netherlands Bv

Inventors

• Batistakis Chrysostomos
• Werkman Roy
• Pisarenco Maxim
• Venugopalan Syam Parayil

Classifications

• H10P74/203

  Structural properties, e.g. testing or measuring thicknesses, line widths, warpage, bond strengths or physical defects · CPC title

• H10P74/23

  characterised by multiple measurements, corrections, marking or sorting processes · CPC title

• G03F7/70625

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

• G03F7/705 Primary

  Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions · CPC title

• G03F7/70616 Primary

  Monitoring the printed patterns · CPC title