Hybrid physics/machine learning modeling of processes

A semiconductor processing tool comprising:

• a chamber;
• a controller for changing a control variable of the semiconductor processing tool, wherein the controller receives, as an input, a difference between a measured output variable from the chamber and an output variable set-point;
• and
• a virtual sensor for generating an estimated system state variable that is used to determine the output variable set-point.

A method of creating a hybrid machine learning model, comprising:

• identifying a first set of cases spanning a first range of process and/or hardware parameters;
• running a physics based simulation for the first set of cases;
• compiling outputs from the physics based simulation;
• using a first machine learning algorithm to generate a reduced order physics simulation model;
• identifying a second set of cases spanning a second range of process and/or hardware parameters, wherein the second set of cases is smaller than the first set of cases;
• running experiments in a lab for the second set of cases;
• compiling experimental outputs from the experiments;
• running physics based simulations for the second set of cases, wherein the physics based simulations use the reduced order physics simulation model;
• compiling model outputs from the simulations;
• and
• correlating the model outputs with the experimental outputs with a second machine learning algorithm to provide the hybrid machine learning model.