Metrology and process control for semiconductor manufacturing

Patent metadata

Field	Value
Publication number	US-11763181-B2
Application number	US-202117400157-A
Country	US
Kind code	B2
Filing date	Aug 12, 2021
Priority date	Jun 14, 2018
Publication date	Sep 19, 2023
Grant date	Sep 19, 2023

Abstract

A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

First claim

What is claimed is:

1. A semiconductor metrology method comprising:
collecting, using a spectrum acquisition tool and in accordance with a first measurement protocol, a baseline set of spectra on a first set of semiconductor wafer targets;
collecting, using a reference metrology tool and in accordance with a second measurement protocol, values of predefined parameters of the first set of semiconductor wafer targets;
for each of one or more predefined sources of spectral variability, collecting a variability set of spectra using the spectrum acquisition tool, and in accordance with the first measurement protocol, on a second set of semiconductor wafer targets corresponding to the first set of semiconductor wafer targets, wherein the variability set of spectra embodies the spectral variability;
creating a training set of data from the collected sets of spectra and parameter values;
using one or more generative models to increase the size of the training set of data; and
using the training set of data to train a prediction model using machine learning and minimize a loss function associated with the prediction model,
wherein the prediction model is configured to be used to predict values for any of the predefined parameters using production spectra of a third set of semiconductor wafer targets, wherein the production spectra are collected using the spectrum acquisition tool and in accordance with the first measurement protocol, and
wherein the loss function is minimized by incorporating, for each of the one or more predefined sources of spectral variability, a term representing the spectral variability.

2. The method according to claim 1 wherein any of the generative models employs a predefined probability function that provides a probability distribution of the data in the training set, and generates new data examples using the probability function, thereby increasing the size of the training set of data.

3. The method according to claim 2 wherein the probability function is explicitly stated.

4. The method according to claim 1 wherein any of the generative models employs a predefined algorithm to determine statistics of the data in the training set and generating new data examples having the same statistics, thereby increasing the size of the training set of data.

5. The method according to claim 4 wherein any of the generative models is a variational autoencoder.

6. The method according to claim 4 wherein any of the generative models employs a generative adversarial network.

7. The method according to claim 1 and further comprising inserting into the prediction model any information and constraints between different features of any of the generative models that reflect underlying physics of the semiconductor wafer targets.

8. The method according to claim 1 wherein the predefined parameters relate to any of physical and chemical characteristics, material properties, electrical properties, and geometric properties of structures at the semiconductor wafer targets.

9. The method according to claim 1 wherein the reference metrology tool is any of a Spectral Ellipsometer (SE), a Spectral Reflectometer (SR), a Polarized Spectral Reflectometer, and an Optical Critical Dimension (OCD) metrology tool.

10. The method according to claim 1 wherein any of the tools are configured for used in an Integrated Metrology system.

11. A semiconductor metrology system comprising:
a spectrum acquisition tool configured to
collect, in accordance with a first measurement protocol, a baseline set of scatterometric spectra on a first set of semiconductor wafer targets, and
for each of one or more predefined sources of spectral variability, collect, in accordance with the first measurement protocol, a variability set of scatterometric spectra on a second set of semiconductor wafer targets corresponding to the first set of semiconductor wafer targets, wherein the variability set of spectra embodies the spectral variability;

a reference metrology tool configured to collect, in accordance with a second measurement protocol, values of predefined parameters of the first set of semiconductor wafer targets;
a training unit configured to
create a training set of data from the collected sets of spectra and parameter values,
using one or more generative models to increase the size of the training set of data, and
use the training set of data to train a prediction model using machine learning and minimize a loss function associated with the prediction model,
wherein the prediction model is configured to be used to predict values for any of the predefined parameters using production spectra of a third set of semiconductor wafer targets, wherein the production spectra are collected using the spectrum acquisition tool and in accordance with the first measurement protocol, and
wherein the loss function is minimized by incorporating, for each of the one or more predefined sources of spectral variability, a term representing the spectral variability.

12. The system according to claim 11 wherein any of the generative models employs a predefined probability function that provides a probability distribution of the data in the training set, and generates new data examples using the probability function, thereby increasing the size of the training set of data.

13. The system according to claim 12 wherein the probability function is explicitly stated.

14. The system according to claim 11 wherein any of the generative models employs a predefined algorithm to determine statistics of the data in the training set and generating new data examples having the same statistics, thereby increasing the size of the training set of data.

15. The system according to claim 14 wherein any of the generative models is a variational autoencoder.

16. The system according to claim 14 wherein any of the generative models employs a generative adversarial network.

17. The system according to claim 11 and further comprising inserting into the prediction model any information and constraints between different features of any of the generative models that reflect underlying physics of the semiconductor wafer targets.

18. The system according to claim 11 wherein the predefined parameters relate to any of physical and chemical characteristics, material properties, electrical properties, and geometric properties of structures at the semiconductor wafer targets.

19. The system according to claim 11 wherein the reference metrology tool is any of a Spectral Ellipsometer (SE), a Spectral Reflectometer (SR), a Polarized Spectral Reflectometer, and an Optical Critical Dimension (OCD) metrology tool.

20. The system according to claim 11 wherein any of the tools are configured for used in an Integrated Metrology system.

Assignees

• Nova Ltd

Inventors

• Rothstein Eitan
• Rubinovich Ilya
• Tal Noam
• Bringoltz Barak
• Kim Yongha
• Broitman Ariel
• Cohen Oded
• Rabinovich Eylon
• Zaharoni Tal
• Yogev Shay
• Kandel Daniel

Classifications

• H10P74/238

  comprising acting in response to an ongoing measurement without interruption of processing, e.g. endpoint detection or in-situ thickness measurement · CPC title

• H10P72/53

  using optical controlling means · CPC title

• G03F7/706841

  Machine learning · CPC title

• G03F7/70616 Primary

  Monitoring the printed patterns · CPC title

• G06N20/00 Primary

  Machine learning · CPC title