Methods and systems for applying run-to-run control and virtual metrology to reduce equipment recovery time

Abstract

Described herein are methods, apparatuses, and systems for reducing equipment repair time. In one embodiment, a computer implemented method includes collecting, with a system, data including test substrate data or other metrology data and fault detection data for maintenance recovery of at least one manufacturing tool in a manufacturing facility and determining, with the system, a relationship between tool parameter settings for the at least one manufacturing tool and at least some collected data including the test substrate data. The method further includes utilizing zero or more virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction and applying multivariate run-to-run (R2R) control modeling to obtain a state estimation including a current operating region of the at least one manufacturing tool based on the test substrate data and obtain at least one tool parameter adjustment for at least one target parameter for the at least one manufacturing tool. Applying multivariate run-to-run (R2R) control modeling to obtain tool parameter adjustments for at least one manufacturing tool occurs after maintenance to reduce maintenance recovery time and to reduce requalification time.

What is claimed is: 
     
         1 . A computer implemented method comprising:
 collecting, with a system, data including test substrate data or other metrology data and fault detection data for maintenance recovery of at least one manufacturing tool in a manufacturing facility;   determining, with the system, a relationship between tool parameter settings for the at least one manufacturing tool and at least some collected data including the test substrate data;   utilizing zero or more virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction; and   applying multivariate run-to-run (R2R) control modeling to obtain a state estimation including a current operating region of the at least one manufacturing tool based on the test substrate data and obtain at least one tool parameter adjustment for at least one target parameter for the at least one manufacturing tool, wherein applying multivariate run-to-run (R2R) control modeling to obtain tool parameter adjustments for at least one manufacturing tool occurs after maintenance to reduce maintenance recovery time and to reduce requalification time.   
     
     
         2 . The computer implemented method of  claim 1 , wherein the R2R control modeling utilizes the following parameters: sensor data obtained from a sensor of the at least manufacturing tool, state at time k, state at time k+1, sensor noise, metrology measurement noise, metrology measurement at time k, a state transition matrix, a process sensitivity matrix, and an observation model matrix. 
     
     
         3 . The computer implemented in method of  claim 1 , wherein the virtual metrology predictive algorithm is tuned prior to or during its use in a tool parameter adjustment event of the at least one tool parameter adjustment. 
     
     
         4 . The computer implemented method of  claim 1 , wherein the collected data includes a thickness profile and a dopant concentration for maintenance recovery of a deposition tool. 
     
     
         5 . The computer implemented method of  claim 4 , wherein the tool parameter adjustments includes adjusting a temperature parameter, lamp power ratios, and gas flow parameters for the deposition tool. 
     
     
         6 . The computer implemented method of  claim 1 , further comprising: 
       determining whether the test substrate data satisfies the at least one target parameter. 
     
     
         7 . A computer-readable storage medium comprising executable instructions to cause a processor to perform operations, the instructions comprising:
 collecting, with a system, data including test substrate data or metrology data and fault detection data for maintenance recovery of at least one manufacturing tool in a manufacturing facility;   determining, with the system, a relationship between tool parameter settings for the at least one manufacturing tool and at least some collected data including the test substrate data;   utilizing zero or more virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction; and   applying multivariate run-to-run (R2R) control modeling to obtain a state estimation including a current operating region of the at least one manufacturing tool based on the test substrate data and obtain at least one tool parameter adjustment for at least one target parameter for the at least one manufacturing tool, wherein applying multivariate run-to-run (R2R) control modeling to obtain tool parameter adjustments for at least one manufacturing tool occurs after maintenance to reduce maintenance recovery time and to reduce requalification time   
     
     
         8 . The computer-readable storage medium of  claim 7 , wherein the R2R control modeling utilizes the following parameters: sensor data obtained from a sensor of the at least manufacturing tool, state at time k, state at time k+1, sensor noise, metrology measurement noise, metrology measurement at time k, a state transition matrix, a process sensitivity matrix, and an observation model matrix. 
     
     
         9 . The computer-readable storage medium of  claim 8 , wherein the virtual metrology predictive algorithm is tuned prior to or during its use in a tool parameter adjustment event of the at least one tool parameter adjustment. 
     
     
         10 . The computer-readable storage medium of  claim 7 , wherein the collected data includes a thickness profile and a dopant concentration for maintenance recovery of a deposition tool. 
     
     
         11 . The computer-readable storage medium of  claim 10 , wherein the tool parameter adjustments includes adjusting a temperature parameter, lamp power ratios, and gas flow parameters for the deposition tool. 
     
     
         12 . The computer implemented method of  claim 7 , further comprising:
 determining whether the test substrate data satisfies the at least one target parameter.   
     
     
         13 . A computer system comprising:
 a memory to store one or more sets of instructions; and   a processor, coupled to the memory, is configured to execute instructions to:   determining, with the system, a relationship between tool parameter settings for the at least one manufacturing tool and at least some collected data including the test substrate data;   utilizing zero or more virtual metrology predictive algorithms and at least some collected data to obtain a metrology prediction;   applying multivariate run-to-run (R2R) control modeling to obtain a state estimation including a current operating region of the at least one manufacturing tool based on the test substrate data and obtain at least one tool parameter adjustment for at least one target parameter for the at least one manufacturing tool, wherein applying multivariate run-to-run (R2R) control modeling to obtain tool parameter adjustments for at least one manufacturing tool occurs after maintenance to reduce maintenance recovery time and to reduce requalification time.   
     
     
         14 . The computer system of  claim 13 , wherein the R2R control modeling utilizes the following parameters: sensor data obtained from a sensor of the at least manufacturing tool, state at time k, state at time k+1, sensor noise, metrology measurement noise, metrology measurement at time k, a state transition matrix, a process sensitivity matrix, and an observation model matrix. 
     
     
         15 . The computer system of  claim 14 , wherein the virtual metrology predictive algorithm is tuned prior to or during its use in a tool parameter adjustment event of the at least one tool parameter adjustment. 
     
     
         16 . The computer system of  claim 13 , wherein the collected data includes a thickness profile and a dopant concentration for maintenance recovery of a deposition tool. 
     
     
         17 . The computer system of  claim 16 , wherein the tool parameter adjustments includes adjusting a temperature parameter, lamp power ratios, and gas flow parameters for the deposition tool. 
     
     
         18 . The computer system of  claim 13 , further comprising:
 determining whether the test substrate data satisfies the at least one target parameter.