Modelling and prediction system with auto machine learning in the production of memory devices

A method, comprising:

• fabricating a plurality of an integrated circuit using a first set of processing parameters;
• creating a first virtual inline quality control data model for the fabrication of the integrated circuits from inline quality control data of test samples of the integrated circuit and post-fabrication test data of the test samples;
• interpolating virtual inline quality control data for the fabrication of the integrated circuits using the first set of processing parameters from the first virtual inline quality control data model and the post-fabrication test data;
• creating a second virtual inline quality control data model for the fabrication of the integrated circuit using the first set of processing parameters from the interpolated virtual inline quality control data and an inline data report for the fabrication of the integrated circuits using the first set of processing parameters, where one or both of creating the first virtual inline quality control data model and creating the second virtual inline quality control data model includes selecting a machine learning model from a plurality of machine learning models including one or more versions of one or more of the machine learning models with different hyperparameter values;
• interpolating virtual inline quality control data for the fabrication of the integrated circuit using the first set of processing parameters from the second virtual inline quality control data model and the inline data report;
• based on the interpolated virtual inline quality control data for the fabrication of the integrated circuit using the first set of processing parameters from the second virtual inline quality control data model and the inline data report, adjusting the first set of processing parameters;
• and
• fabricating the integrated circuit using the adjusted first set of processing parameters.

A system, comprising:

• one or more processors, the one or more processors configured to:
• receive, from a fabrication facility, inline quality control data of test samples from manufacture of an integrated circuit;
• receive post-manufacturing test data of the test samples;
• create a first virtual inline quality control data model for the manufacture of the integrated circuit from the inline quality control data and the post-manufacturing test data;
• interpolate virtual inline quality control data for the manufacture of the integrated circuit from the first virtual inline quality control data model and the post-manufacturing test data;
• receive, from the fabrication facility, an inline data report for the manufacture of the integrated circuit;
• create a second virtual inline quality control data model for the manufacture of the integrated circuit from the interpolated virtual inline quality control data and the inline data report, where one or both of creating the first virtual inline quality control data model and creating the second virtual inline quality control data model includes selecting a machine learning model from a plurality of machine learning models including one or more versions of one or more of the machine learning models with different hyperparameter values;
• and
• provide the second virtual inline quality control data model to the fabrication facility.