Determination of substrate layer thickness with polishing pad wear compensation

Patent metadata

Field	Value
Publication number	US-12090599-B2
Application number	US-202318365527-A
Country	US
Kind code	B2
Filing date	Aug 4, 2023
Priority date	Jun 24, 2020
Publication date	Sep 17, 2024
Grant date	Sep 17, 2024

Abstract

A method of training a neural network includes obtaining two ground truth thickness profiles a test substrate, obtaining two thickness profiles for the test substrate as measured by an in-situ monitoring system while the test substrate is on polishing pads of different thicknesses, generating an estimated thickness profile for another thickness value that is between the two thickness values by interpolating between the two profiles, and training a neural network using the estimated thickness profile.

First claim

What is claimed is:

1. A method of training a neural network, comprising:
for each test substrate of a plurality of test substrates having a different thickness profile, obtaining a ground truth thickness profile for the test substrate;
obtaining a first thickness value;
for each test substrate of the plurality of test substrates, obtaining a first measured thickness profile corresponding to the test substrate being measured by an in-situ monitoring system while on a polishing pad of a first thickness corresponding to the first thickness value;
obtaining a second thickness value;
for each test substrate of the plurality of test substrates, obtaining a second measured thickness profile corresponding to the test substrate being measured by the in-situ monitoring system while on a polishing pad of a second thickness corresponding to the second thickness value;
for each test substrate of the plurality of test substrates, generating an estimated third thickness profile for a third thickness value that is between the first thickness value and the second thickness value by interpolating between the first measured thickness profile for the test substrate and the second measured thickness profile; and
training a neural network that has a plurality of input nodes and a plurality of output nodes by, for each test substrate, applying the estimated third thickness profile to a multiplicity of input nodes from the plurality of input nodes, applying the third thickness value to an input node from the plurality of input nodes or to an intermediate node in the neural network, and applying the ground truth thickness profile to a plurality of output nodes while the neural network is in a training mode.

2. The method of claim 1 , wherein obtaining the first and second thickness values include measuring a thickness of the polishing pad of the first thickness and measuring a thickness of the polishing pad of the second thickness.

3. The method of claim 2 , wherein measuring thickness of the polishing pad of the first thickness and measuring thickness of the polishing pad of the second thickness comprises measuring with a profilometer.

4. The method of claim 1 , wherein obtaining the first or second measured thickness profiles further includes placing the test substrate on a polishing pad of the first or second thickness and scanning the test substrate with an in-situ monitoring system.

5. The method of claim 1 , wherein training the neural network further includes applying the first or second thickness and the first or second measured thickness profiles to a plurality of input nodes and the ground truth thickness profile to a plurality of output nodes while the neural network is in the training mode.

6. The method of claim 1 , wherein training the neural network further includes applying a fourth thickness and an estimated fourth thickness profile to a plurality of input nodes and the ground truth thickness profile to a plurality of output nodes while the neural network is in the training mode.

7. The method of claim 1 , wherein the interpolation is a linear interpolation.

8. The method of claim 1 , comprising applying the third thickness value directly to the intermediate node.

9. The method of claim 1 , comprising applying the third thickness value to the input node from the plurality of input nodes.

10. The method of claim 1 , wherein the first measured thickness profile and the second measured thickness profile are thicknesses of a conductive layer on the test substrate.

11. The method of claim 10 , wherein the in-situ monitoring system comprises an eddy current monitoring system.

12. The method of claim 10 , wherein obtaining the ground truth thickness profile comprises an electrical impedance measurement.

13. The method of claim 12 , wherein the electrical impedance measurement comprises measuring using a four point probe.

Assignees

• Applied Materials Inc

Inventors

• Xu Kun
• Cherian Benjamin
• Qian Jun
• Shrestha Kiran Lall

Classifications

• H10W99/00

  Subject matter not provided for in other groups of this subclass · CPC title

• H10P72/00

  Handling or holding of wafers, substrates or devices during manufacture or treatment thereof · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• B24B49/183

  Wear compensation without the presence of dressing tools · CPC title