System for wafer quality predictive modeling based on multi-source information with heterogeneous relatedness

Patent metadata

Field	Value
Publication number	US-9395408-B2
Application number	US-201213677542-A
Country	US
Kind code	B2
Filing date	Nov 15, 2012
Priority date	Oct 15, 2012
Publication date	Jul 19, 2016
Grant date	Jul 19, 2016

Abstract

The present invention generally relates to the monitoring and controlling of a semiconductor manufacturing environment and, more particularly, to methods and systems for virtual meteorology (VM) applications based on data from multiple tools having heterogeneous relatedness. The methods and systems leverage the natural relationship of the multiple tools and take advantage of the relationship embedded in process variables to improve the prediction performance of the VM predictive wafer quality modeling. The prediction results of the methods and systems can be used as a substitute for or in conjunction with actual metrology samples in order to monitor and control a semiconductor manufacturing environment, and thus reduce delays and costs associated with obtaining actual physical measurements.

First claim

What is claimed is:

1. A system for predicting quality of wafers produced by production equipment that includes multiple chambers in each of which multiple wafers are simultaneously processed, comprising:
means for measuring process variables of each of the wafers;
means for sending the process variables to a central database;
a virtual metrology machine implemented on a computer configured for
receiving the process variables of each of the wafers and historical measurements;
representing respective sides of the multiple chambers as wafer quality predictive modeling tasks based on the historical measurements:
grouping the wafer quality predictive modeling tasks according to heterogeneous relatedness of the production equipment, said means for grouping including, for each chamber in which multiple wafers are simultaneously processed, means for grouping wafer quality predictive modeling tasks for all of the respective sides of the each chamber, each chamber of the multiple chambers being treated as a group of one or more wafer quality predictive modeling tasks;
partitioning the process variables into two sets;
generating a prediction model that accommodates the grouping of the wafer quality predictive modeling tasks from the means for grouping and the partitioned process variables from the means for partitioning;
predicting a quality of each of the wafers produced in individual sides of the multiple chambers based on the prediction model generated by the means for generating;

means for sending the predicted quality of each of the wafers to an advanced process controller and the central database;
an actual metrology tool for measuring an actual quality of at least one sample of the wafers;
means for sending the actual quality of at least one sample of the wafers to the advanced process controller and the virtual metrology machine;
means for updating the virtual metrology machine with the actual quality of at least one sample of the wafers;
means for determining a feedback control by the advanced process controller; and
means for processing the wafers by the production equipment in accordance with the feedback control;
wherein for each chamber in which multiple wafers are simultaneously processed, each side of the each chamber processes only one wafer at a time.

2. The system of claim 1 , wherein one of the two sets of the process variables represents independent variables and one of the two sets of the process variables represents dependent variables.

3. The system of claim 2 , wherein the prediction model is generated as a function of coefficient vectors for the independent variables and the dependent variables.

4. The system of claim 3 , wherein the prediction model accommodates the grouping of the wafers by connecting the coefficient vectors for the independent variables and the dependent variables through a transformation matrix and imposing similarity on the coefficient vectors for one or more of the independent variables and the dependent variables.

5. The system of claim 1 further comprising:
means for optimizing the prediction model by implementing a block coordinate descent.

6. The system of claim 5 , wherein the block coordinate descent includes an accelerated update.

7. The system of claim 1 , wherein the process variables are indicative of production of each of the wafers processed by the production equipment.

8. The system of claim 1 , wherein the historical measurements are examples of actual wafer quality measurements.

9. The system of claim 2 , wherein the independent variables are selected from the group consisting of pressure, power, temperature, and gas flows, each of said group members being subject to the advanced process controller.

10. The system of claim 2 , wherein the dependent variables are selected from the group consisting of impedance, electric bias, and throttle valve positions, and have an impact on the quality of the wafers being heavily dependent on the independent variables.

11. The system of claim 1 , wherein the feedback control is determined based on the predicted quality.

12. The system of claim 1 , wherein the feedback control is determined based on the predicted quality and the actual quality.

13. The system of claim 1 , wherein the quality of the wafers is measured in terms selected from the group consisting of deposition thickness, resistance, stress, and refractive index.

Assignees

• Globalfoundries Inc

Inventors

• Zhu Yada
• He Jingrui
• Baseman Robert Jeffrey

Classifications

• H10P74/207

  Electrical properties, e.g. testing or measuring of resistance, deep levels or capacitance-voltage characteristics · CPC title

• H10P74/23

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

• G01R31/2831 Primary

  Testing of materials or semi-finished products, e.g. semiconductor wafers or substrates (G01R31/318511 takes precedence; testing during manufacture H10P74/00) · CPC title

• H01L22/20

  Electricity · mapped topic

• H01L2924/0002

  Electricity · mapped topic