Multivariate monitoring of a batch manufacturing process

• US 9,429,939 B2
• Filed: 04/06/2012
• Issued: 08/30/2016
• Est. Priority Date: 04/06/2012
• Status: Active Grant

• Current Assignee: MKS Instruments AB (Sartorius AG)
• Original Assignee: MKS Instruments Incorporated
• Inventors: McCready, Christopher Peter
• Primary Examiner(s): HENSON, MISCHITA L
• Application Number:
  US13/441,513
• Publication Number:
  US 20130268238A1
• Time in Patent Office: 1,607 Days
• US Class Current: 1/1
• 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
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Abstract

A method, controller, and system for monitoring a manufacturing process are described. Measured values of multiple variables, including dependent variables, manipulated variables, or both, are received. Future values of the manipulated variables, future values of the dependent variables, or both, are predicted. A multivariate analysis is performed on a combination of (1) the measured values of the variables and (2) the future values of the manipulated variables, the future values of the dependent variables, or both, to generate multivariate statistics.

Claims

1. A computer-implemented method for monitoring a manufacturing process in a processing facility, the method comprising:

receiving, via a computing device from the processing facility, measured values of a plurality of variables of the manufacturing process, the plurality of variables including at least one of manipulated variables or dependent variables, the plurality of variables representing physical parameters of the manufacturing process;

determining, with the computing device, future values of the manipulated variables or future values of the dependent variables, or a combination thereof, wherein the manipulated variables represent process parameters whose values are directly assignable during the manufacturing process and the dependent variables represent process parameters whose values are dependent on one or more process conditions;

creating, with the computing device, an unfolded data matrix using observation-wise unfolding of a batch data array, such that each row of the unfolded matrix includes observation of the plurality of variables at a unique time sample within a finite duration;

performing multivariate analysis, via the computing device, on the unfolded data matrix comprising a combination of (1) the measured values of the variables and (2) at least one of the future values of the manipulated variables or the future values of the dependent variables to generate a plurality of multivariate statistics, the plurality of multivariate statistics represent a trajectory of measured past, current and estimated future behavior of the manufacturing process; and

causing, by the computing device, adjustment to one or more of the physical parameters of the manufacturing process in the processing facility based on the plurality of multivariate statistics to prevent deviation of the trajectory from a desired trajectory.

2. The computer-implemented method of claim 1, wherein the manufacturing process is a batch-type manufacturing process associated with the finite duration.

3. The computer implemented method of claim 2, wherein the variables are measured or known up to a current maturity point within the finite duration of the batch-type manufacturing process.

4. The computer implemented method of claim 3, further comprising estimating the future values of the dependent variables after the current maturity point to the end of the finite duration.

5. The computer implemented method of claim 1, further comprising predicting a future fault of the manufacturing process based on the trajectory.

6. The computer implemented method of claim 1, wherein the dependent variables are not directly assignable during the manufacturing process.

7. The computer implemented method of claim 1, wherein the values of the dependent variables are dependent on at least one of:

(1) past values of the dependent variables, (2) past values of the manipulated variables, or (3) future values of the manipulated variables.

8. The computer implemented method of claim 1, wherein the future values of the manipulated variables represent known values for setting the manipulated variables at one or more future points in time.

9. The computer implemented method of claim 1, wherein each of the plurality of multivariate statistics comprises at least a multivariate score, a Hotelling'"'"'s T² value, a DModX value, or any combination thereof.

10. The computer implemented method of claim 9, wherein the multivariate score comprises a principal components analysis t-score or a partial least squares analysis t-score.

11. The computer implemented method of claim 1, further comprising comparing the plurality of multivariate statistics with a time-varying reference model of the manufacturing process to detect a fault in the manufacturing process.

12. The computer implemented method of claim 1, further comprising predicting the future values of the dependent variables using at least one of an imputation method or a regression method based on at least one of the measured values of the plurality of variables or the future values of the manipulated variables.

13. The computer implemented method of claim 1, further comprising:

receiving a second set of future values of the manipulated variables, which represent hypothesized values for setting the manipulated variables; and

performing multivariate analysis on a combination of (1) the measured values of the variables, (2) the second set of future values of the manipulated variables and (3) the future values of the dependent variables to generate a second plurality of multivariate statistics.

14. The computer implemented method of claim 13, wherein the second plurality of multivariate statistics predict an effect of the second set of future values of the manipulated variables on the manufacturing process.

15. A multivariate monitor for a batch-type manufacturing process associated with a finite duration, wherein the batch-type manufacturing process is performed in a processing facility and the monitor is implemented on a computing device in electrical communication with the processing facility, the monitor comprising:

one or more sensors, coupled to the processing facility, for measuring values of a plurality of variables of the manufacturing process up to a current maturity point of the finite duration, the plurality of variables including at least one of manipulated variables or dependent variables, wherein the plurality of variables represent physical parameters of the manufacturing process;

a hardware-based prediction module of the computing device for computing future values of the dependent variables after the current maturity point, wherein the dependent variables represent one or more process parameters whose values are not directly assignable;

a memory of the computing device for storing an unfolded data matrix created using observation-wise unfolding of a batch data array, such that each row of the unfolded matrix includes observation of the plurality of variables at a unique time sample within the finite duration; and

a hardware-based analysis module of the computing device for performing multivariate analysis on the unfolded data matrix comprising measured values of the variables and the future values of the dependent variables to generate a plurality of multivariate statistics, the plurality of multivariate statistics represent a trajectory of measured past, current and estimated future behavior of the batch-type manufacturing process over at least a portion of the finite duration,

wherein the computing device is adapted to cause adjustment to one or more of the physical parameters of the manufacturing process in the processing facility based on the plurality of multivariate statistics to prevent deviation of the trajectory from a desired trajectory.

16. The multivariate monitor of claim 15, wherein the trajectory includes predicted future behavior of the batch-type manufacturing process from the current maturity point to the end of the finite duration.

17. The multivariate monitor of claim 15, further comprising a fault detection module for predicting a future fault of the manufacturing process based on the trajectory.

18. The multivariate monitor of claim 15, wherein the prediction module is further configured to determine future values of the manipulated variables representative of a set of known values for setting the manipulated variables at one or more future points in time subsequent to the current maturity point, the manipulated variables being directly assignable during the manufacturing process.

19. The multivariate monitor of claim 18, wherein the analysis module performs multivariate analysis on the unfolded data matrix comprising a combination of the measured values of the variables, the future values of the manipulated variables and the future values of the dependent variables to generate the plurality of the multivariate statistics.

20. The multivariate monitor of claim 18, wherein the prediction module computes the future values of the dependent variables using at least one of a regression method or an imputation method based on at least one of the measured values of the plurality of variables or the future values of the manipulated variables.

21. The multivariate monitor of claim 15, wherein each of the plurality of the multivariate statistics comprises at least a multivariate score, a Hotelling'"'"'s T² value, a DModX value, or any combination thereof.