Predictive modeling of a manufacturing process using a set of trained inverted models

Abstract

Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data defining an attribute of a semiconductor device manufactured by at least one semiconductor device manufacturing process performed within at least one processing chamber, wherein the expected output data corresponds to an unexplored portion of a process space associated with the at least one semiconductor device manufacturing process, and identifying expected input data by using the expected output data as input to a plurality of homogeneous inverted machine learning models, wherein each inverted machine learning model of the plurality of homogeneous inverted machine learning models is trained to determine, by performing linear extrapolation based on the expected output data, a respective set of input data of a plurality of sets of input data for configuring the semiconductor device manufacturing process to manufacture the semiconductor device.

What is claimed is: 
     
         1 . A method comprising:
 receiving, by a processing device, expected output data defining an attribute of a semiconductor device manufactured by at least one semiconductor device manufacturing process performed within at least one processing chamber, wherein the expected output data corresponds to an unexplored portion of a process space associated with the at least one semiconductor device manufacturing process; and   identifying, by the processing device, expected input data by using the expected output data as input to a plurality of homogeneous inverted machine learning models, wherein each inverted machine learning model of the plurality of homogeneous inverted machine learning models is trained to determine, by performing linear extrapolation based on the expected output data, a respective set of input data of a plurality of sets of input data for configuring the semiconductor device manufacturing process to manufacture the semiconductor device.   
     
     
         2 . The method of  claim 1 , further comprising:
 combining, by the processing device, at least a first set of input data of the plurality of sets of input data with a second set of input data of the plurality of sets of input data to generate a set of semiconductor device manufacturing process inputs defining an extrapolated solution corresponding to the unexplored portion of the process space, wherein the set of semiconductor device manufacturing process inputs comprises a plurality of candidate values; and   storing, by the processing device, the set of semiconductor device manufacturing process inputs in a storage device.   
     
     
         3 . The method of  claim 2 , further comprising clustering, by the processing device, the first set of input data and the second set of input data into a plurality of groups, wherein each group of the plurality of groups comprises a respective value for the first set of input data and a respective value for the second set of input data. 
     
     
         4 . The method of  claim 2 , wherein the plurality of candidate values comprises a range of values for the first set of input data and a range of values for the second set of input data. 
     
     
         5 . The method of  claim 1 , wherein each set of input data comprises data related to performing the semiconductor device manufacturing process that is indicative of at least one of: time, energy, temperature, voltage, gas flow rate, wafer spin speed, distance, pressure, a precursor, a reactant, or a dilutant. 
     
     
         6 . The method of  claim 1 , wherein the expected output data for the manufacturing process comprises one or more values that indicate a layer thickness, a layer uniformity, or a structural width of a product that will be output by the manufacturing process. 
     
     
         7 . The method of  claim 1 , wherein each set of input data of the plurality of sets of input data comprises at least one of: a different hyperparameter, a different initialization value, or different training data. 
     
     
         8 . The method of  claim 1 , wherein the plurality of homogeneous inverted machine learning models comprises a plurality of Feed Forward Neural Networks. 
     
     
         9 . The method of  claim 8 , wherein each inverted machine learning model of the plurality of homogeneous inverted machine learning models comprises an output layer and a plurality of hidden layers to model the semiconductor device manufacturing process, and wherein the plurality of hidden layers comprises a polynomial function and the output layer comprises a linear activation function. 
     
     
         10 . The method of  claim 1 , further comprising:
 providing, by the processing device for display, a plurality of candidate input value sets, wherein each candidate input value set of the plurality of candidate input value sets corresponds to the expected output data for the semiconductor device manufacturing process;   receiving, by the processing device, a user selection of a candidate input value set of the plurality of candidate input value sets to obtain a selected candidate input value set; and   initiating, by the processing device, a run of the semiconductor device manufacturing process using the selected candidate input value set.   
     
     
         11 . A system comprising:
 a memory; and   a processing device communicably coupled to the memory, the processing device to perform operations comprising:
 receiving expected output data defining an attribute of a semiconductor device manufactured by at least one semiconductor device manufacturing process performed within at least one processing chamber, wherein the expected output data corresponds to an unexplored portion of a process space associated with the at least one semiconductor device manufacturing process; and 
 identifying expected input data by using the expected output data as input to a plurality of homogeneous inverted machine learning models, wherein each inverted machine learning model of the plurality of homogeneous inverted machine learning models is trained to determine, by performing linear extrapolation based on the expected output data, a respective set of input data of a plurality of sets of input data for configuring the semiconductor device manufacturing process to manufacture the semiconductor device. 
   
     
     
         12 . The system of  claim 11 , wherein the operations further comprise:
 combining at least a first set of input data of the plurality of sets of input data with a second set of input data of the plurality of sets of input data to generate a set of semiconductor device manufacturing process inputs defining an extrapolated solution corresponding to the unexplored portion of the process space, wherein the set of semiconductor device manufacturing process inputs comprises a plurality of candidate values; and   storing, by the processing device, the set of semiconductor device manufacturing process inputs in a storage device.   
     
     
         13 . The system of  claim 12 , wherein the operations further comprise clustering the first set of input data and the second set of input data into a plurality of groups, and wherein each group of the plurality of groups comprises a respective value for the first set of input data and a respective value for the second set of input data. 
     
     
         14 . The system of  claim 12 , wherein the plurality of candidate values comprises a range of values for the first set of input data and a range of values for the second set of input data. 
     
     
         15 . The system of  claim 11 , wherein each set of input data comprises data related to performing the semiconductor device manufacturing process that is indicative of at least one of: time, energy, temperature, voltage, gas flow rate, wafer spin speed, distance, pressure, a precursor, a reactant, or a dilutant. 
     
     
         16 . The system of  claim 11 , wherein the expected output data for the manufacturing process comprises one or more values that indicate a layer thickness, a layer uniformity, or a structural width of a product that will be output by the manufacturing process. 
     
     
         17 . The system of  claim 11 , wherein each set of input data of the plurality of sets of input data comprises at least one of: a different hyperparameter, a different initialization value, or different training data. 
     
     
         18 . The system of  claim 11 , wherein the plurality of homogeneous inverted machine learning models comprises a plurality of Feed Forward Neural Networks. 
     
     
         19 . The system of  claim 18 , wherein each inverted machine learning model of the plurality of homogeneous inverted machine learning models comprises an output layer and a plurality of hidden layers to model the semiconductor device manufacturing process, and wherein the plurality of hidden layers comprises a polynomial function and the output layer comprises a linear activation function. 
     
     
         20 . The system of  claim 11 , wherein the operations further comprise:
 providing, for display, a plurality of candidate input value sets, wherein each candidate input value set of the plurality of candidate input value sets corresponds to the expected output data for the semiconductor device manufacturing process;   receiving a user selection of a candidate input value set of the plurality of candidate input value sets to obtain a selected candidate input value set; and   initiating a run of the semiconductor device manufacturing process using the selected candidate input value set.