METHOD OF MONITORING PROCESSING SYSTEM FOR PROCESSING SUBSTRATE

• US 20180224817A1
• Filed: 02/08/2017
• Published: 08/09/2018
• Est. Priority Date: 02/08/2017
• Status: Active Grant

• Current Assignee: United Microelectronics Corporation
• Original Assignee: United Microelectronics Corporation
• Inventors: Shih, Lian-Hua, Chang, Chia-Chi, Lin, Li-Ting, Hsieh, Ching-Hsing, Chung, Feng-Chi, Chang, Meng-Chih, Wang, Ming-Tung, Chang, Chiu-Ping, Yang, Yung-Yu
• Granted Patent: US 10,606,253 B2
• Time in Patent Office: Days
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

Abstract

A method of monitoring a processing system for processing a substrate is provided. The method includes the following steps: acquiring data from the processing system for a plurality of parameters, the data including a plurality of data values; grouping the parameters into a plurality of sub-groups, each of the sub-groups including a plurality of correlated parameters; constructing a principle components analysis (PCA) model from the data values for the correlated parameters in a first one of the sub-groups, including normalizing the data values in the first one of the sub-groups with a first weighting factor and a second weighting factor, wherein the first weighting factor is different from the second weighting factor; and determining a statistical quantity using the PCA model.

Claims

1. A method of monitoring a processing system for processing a substrate, comprising:

acquiring data from the processing system for a plurality of parameters, the data comprising a plurality of data values;

grouping the parameters into a plurality of sub-groups, each of the sub-groups comprising a plurality of correlated parameters;

constructing a principle components analysis (PCA) model from the data values for the correlated parameters in a first one of the sub-groups, comprising;

normalizing the data values in the first one of the sub-groups with a first weighting factor and a second weighting factor, wherein the first weighting factor is different from the second weighting factor; and

determining a statistical quantity using the PCA model.

2. The method according to claim 1, wherein normalizing the data values in the first one of the sub-groups comprises:

applying the first weighting factor to a first one of the data values and the second weighting factor to a second one of the data values in the first one of the sub-groups.

3. The method according to claim 1, further comprising:

determining a control limit for the statistical quantity; and

comparing the statistical quantity to the control limit, wherein a process fault has occurred when the statistical quantity exceeds the control limit.

4. The method according to claim 1, wherein each one of the sub-groups comprises less than ten correlated parameters.

5. The method according to claim 1, wherein the data comprises at least one of a temperature of a heat exchanger, a resistivity of a heat exchanger, a distance from a heater to a shower head, a power output of a heater, a temperature reading of a heater, an RF forward power, an RF impedance, an electrode voltage, an RF reflective power, an argon flow rate, a helium flow rate during an atom transfer radical polymerization (ATRP), a helium flow rate of an oxygenator, an oxygen flow rate, a helium flow rate, a flow rate of an oxygenator, a flow rate of an atom transfer radical polymerization, a chamber pressure reading, a position of a throttle valve, and a chamber pressure supplied.

6. The method according to claim 1, wherein the statistical quantity is a Hotelling T² parameter.

7. A method of monitoring a processing system for processing a substrate, comprising:

acquiring data from the processing system for a plurality of parameters in a first period of time, the data comprising a plurality of data values;

grouping the parameters into a plurality of sub-groups, each of the sub-groups comprising a plurality of correlated parameters;

constructing a principle components analysis (PCA) model from a first set of data values for the correlated parameters in a first one of the sub-groups;

determining a statistical quantity using the PCA model;

performing a prevention maintenance step to the processing system after the first period of time;

acquiring a second set of data values for the correlated parameters in the first one of the sub-groups from the processing system in a second period of time after performing the prevention maintenance step; and

determining an updated statistical quantity using a combination of the second set of data values and the PCA model.

8. The method according to claim 7, wherein the prevention maintenance step comprises at least one of cleaning the processing system, replacing a component of the processing system, and repairing a component of the processing system.

9. The method according to claim 7, wherein constructing the PCA model comprises:

normalizing the first set of data values in the first one of the sub-groups with a first weighting factor and a second weighting factor, wherein the first weighting factor is different from the second weighting factor.

10. The method according to claim 9, wherein constructing the PCA model further comprises:

applying the first weighting factor to a first one of the first set of data values and the second weighting factor to a second one of the first set of data values in the first one of the sub-groups.

11. The method according to claim 7, further comprising:

determining a control limit for the updated statistical quantity; and

comparing the updated statistical quantity to the control limit, wherein a process fault has occurred when the updated statistical quantity exceeds the control limit.

12. The method according to claim 7, wherein each one of the sub-groups comprises less than ten correlated parameters.

13. The method according to claim 7, wherein the data comprises at least one of a temperature of a heat exchanger, a resistivity of a heat exchanger, a distance from a heater to a shower head, a power output of a heater, a temperature reading of a heater, an RF forward power, an RF impedance, an electrode voltage, an RF reflective power, an argon flow rate, a helium flow rate during an atom transfer radical polymerization (ATRP), a helium flow rate of an oxygenator, an oxygen flow rate, a helium flow rate, a flow rate of an oxygenator, a flow rate of an atom transfer radical polymerization, a chamber pressure reading, a position of a throttle valve, and a chamber pressure supplied.

14. The method according to claim 7, wherein the statistical quantity is a Hotelling T² parameter.

15. The method according to claim 7, wherein the second set of data values comprise a first group of data values and a second group of data values acquired subsequent to the first group of data values, the method further comprising:

determining whether a current data value of the second group of data values exceeds an estimated statistical quantity or not, wherein the estimated statistical quantity is determined using a combination of precious data values of the second set of data values acquired in the second period of time and the PCA model; and

constructing an updated PCA model using a combination of the current data value, the previous data values and the PCA model for determining the updated statistical quantity when the current data value does not exceed the estimated statistical quantity.