Self-supervised representation learning for interpretation of OCD data

Patent metadata

Field	Value
Publication number	US-11747740-B2
Application number	US-202117790765-A
Country	US
Kind code	B2
Filing date	Jan 6, 2021
Priority date	Jan 6, 2020
Publication date	Sep 5, 2023
Grant date	Sep 5, 2023

Abstract

A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

First claim

The invention claimed is:

1. A method for OCD metrology, comprising:
receiving multiple first sets of scatterometric data;
dividing each of the multiple first sets of scatterometric data into k sub-vectors;
training, in a self-supervised manner, k 2 auto-encoder neural networks, mapping each of the k sub-vectors to each other, wherein the k auto-encoder neural networks include k 2 respective encoder neural networks each having at least one internal bottleneck layer;
receiving multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data, measured from multiple respective wafer patterns; and
training a transfer neural network (NN) having initial layers including a parallel arrangement of the k 2 encoder neural networks, wherein the transfer NN training comprises training one or more final layers that follow the bottleneck layers of the encoder neural networks, and wherein target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

2. The method of claim 1 , wherein setting the multiple second sets of scatterometric data as the feature input for the transfer NN training comprises providing, at an input layer of the transfer NN, for each second set of scatterometric data, k sets of each of k sub-vectors of the second set of scatterometric data.

3. The method of claim 1 , wherein the multiple second sets of scatterometric data include a subset of the multiple first sets of scatterometric data.

4. The method of claim 1 , wherein training the transfer neural network comprises minimizing a loss function with respect to the multiple sets of reference parameters, and wherein the loss function is a mean squared error (MSE) function.

5. The method of claim 1 , wherein the multiple sets of reference parameters are measured with high accuracy metrology by one or more of a CD scanning electron microscope (CD-SEM), an atomic force microscope (AFM), a cross-section tunneling electron microscope (TEM), or an X-ray metrology tool.

6. The method of claim 1 , wherein the multiple respective wafer patterns are located on one or more wafers.

7. The method of claim 1 , wherein the multiple sets of scatterometric data are measured by two or more measurement channels.

8. A system for OCD metrology compromising a processor having non-transient memory, the memory including instructions that when executed by the processor cause the processor to implement steps of:
receiving multiple first sets of scatterometric data;
dividing each of the multiple first sets of scatterometric data into k sub-vectors;
training, in a self-supervised manner, k 2 auto-encoder neural networks, mapping each of the k sub-vectors to each other, wherein the k 2 auto-encoder neural networks include k 2 respective encoder neural networks each having at least one internal bottleneck layer;
receiving multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data, measured from multiple respective wafer patterns; and
training a transfer neural network (NN) having initial layers including a parallel arrangement of the k 2 encoder neural networks, wherein the transfer NN training comprises training one or more final layers that follow the bottleneck layers of the encoder neural networks, and wherein target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

9. The system of claim 8 , wherein setting the multiple second sets of scatterometric data as the feature input for the transfer NN training comprises providing at an input layer of the transfer NN, for each second set of scatterometric data, k sets of each of k sub-vectors of the second set of scatterometric data.

10. The system of claim 8 , wherein the multiple second sets of scatterometric data include a subset of the multiple first sets of scatterometric data.

11. The system of claim 8 , wherein training the transfer neural network comprises minimizing a loss function with respect to the multiple sets of reference parameters, and wherein the loss function is a mean squared error (MSE) function.

12. The system of claim 8 , wherein the multiple sets of reference parameters are measured with high accuracy metrology by one or more of a CD scanning electron microscope (CD-SEM), an atomic force microscope (AFM), a cross-section tunneling electron microscope (TEM), or an X-ray metrology tool.

13. The system of claim 8 , wherein the multiple respective wafer patterns are located on one or more wafers.

14. The system of claim 8 , wherein the multiple sets of scatterometric data are measured by two or more measurement channels.

Assignees

• Nova Ltd

Inventors

• Yacoby Ran
• Sturlesi Boaz

Classifications

• G03F7/70625 Primary

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

• G03F7/70508

  Data handling in all parts of the microlithographic apparatus, e.g. handling pattern data for addressable masks or data transfer to or from different components within the exposure apparatus · CPC title

• G06N3/08

  Learning methods · CPC title

• G01N21/9501 Primary

  Semiconductor wafers (manufacturing processes per se of semiconductor devices implementing a measuring step H10P74/20) · CPC title

• G01B11/02 Primary

  for measuring length, width or thickness (G01B11/08 takes precedence) · CPC title