Metrology and process control for semiconductor manufacturing

Patent metadata

Field	Value
Publication number	US-11093840-B2
Application number	US-201916973092-A
Country	US
Kind code	B2
Filing date	Jun 14, 2019
Priority date	Jun 14, 2018
Publication date	Aug 17, 2021
Grant date	Aug 17, 2021

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; and
using the collected sets of spectra and parameter values 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 the predefined sources of spectral variability include tool variability.

3. The method according to claim 2 wherein the collecting the variability spectra comprises collecting the variability spectra from a selected one of the semiconductor wafer targets using multiple and identical ones of the spectrum acquisition tool.

4. The method according to claim 1 wherein the predefined sources of spectral variability include measurement repeatability.

5. The method according to claim 4 wherein the collecting the variability spectra comprises collecting the variability spectra from a selected one of the semiconductor wafer targets using the spectrum acquisition tool at multiple different points in time.

6. The method according to claim 1 wherein the first and second measurement protocols differ in any of numbers of channels, illumination angles, targets, and signals acquired from the same target.

7. The method according to claim 1 and further comprising:
collecting production scatterometric spectra during the fabrication of a production semiconductor wafer; and
producing, using the prediction model, a prediction value for any of the predefined parameters based on the production scatterometric spectra.

8. The method according to claim 7 and further comprising providing input to a semiconductor manufacturing tool for controlling operation of the semiconductor manufacturing tool during the fabrication of the production semiconductor wafer.

9. 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; and
a training unit configured to use the collected sets of spectra and parameter values 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.

10. The system according to claim 9 wherein the predefined sources of spectral variability include tool variability.

11. The system according to claim 10 wherein the spectrum acquisition tool is configured to collect the variability spectra from a selected one of the semiconductor wafer targets using multiple and identical ones of the spectrum acquisition tool.

12. The system according to claim 9 wherein the predefined sources of spectral variability include measurement repeatability.

13. The system according to claim 12 wherein the spectrum acquisition tool is configured to collect the variability spectra from a selected one of the semiconductor wafer targets using the spectrum acquisition tool at multiple different points in time.

14. The system according to claim 9 wherein the first and second measurement protocols differ in any of numbers of channels, illumination angles, targets, and signals acquired from the same target.

15. The system according to claim 9 wherein the spectrum acquisition tool is configured to collect production scatterometric spectra during the fabrication of a production semiconductor wafer, and further comprising a prediction unit configured to produce, using the prediction model, a prediction value for any of the predefined parameters based on the production scatterometric spectra.

16. The system according to claim 15 and further comprising a process control unit configured to provide input, based on the prediction value, to a semiconductor manufacturing tool for controlling operation of the semiconductor manufacturing tool during the fabrication of the production semiconductor wafer.

Assignees

• Nova Measuring Instr 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

• G03F7/70616 Primary

  Monitoring the printed patterns · CPC title

• G03F7/705 Primary

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

• G01B2210/56

  Measuring geometric parameters of semiconductor structures, e.g. profile, critical dimensions or trench depth · CPC title