Real-time AI-based quality assurance for semiconductor production machines

A method for process control in association with a production system that generates production data, wherein the production data comprises non-homogeneous machine parameters and one of:

• maintenance data, quality test data, and product and process data, comprising:
• prior to training a neural network to model an operation of a machine in the production system:
• without reference to the production data of the production system, applying domain-specific knowledge about a given machine type or production process step in the production system to identify one or more process variables and, for a given process variable so identified, identifying a feature;
• isolating, from received production data, informative data associated with the identified feature;
• using at least the informative data isolated from the production data, training the neural network to model the operation of a given machine in the production system, wherein the training includes data augmentation;
• following completion of the training, receiving given process and machine data associated with the given machine and, in real-time, using the neural network to generate a prediction associated with a given product processed by the given machine in the production system, the prediction having an associated reliability index;
• based on the prediction, generating signaling to control an advanced process control (APC) associated with the production system;
• and
• applying the signaling to the APC, thereby controlling a workflow associated with the given machine.