Using defect models to estimate defect risk and optimize process recipes

A system comprising:

• a memory and
• a processing device, operatively coupled to the memory, to perform operations comprising:
• receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing, wherein the data associated with the process comprises at least one of:
• an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings;
• and
• obtaining an output by applying the data associated with the process to the trained machine learning model, wherein the output is representative of the defect impact with respect to the at least one defect type.

A non-transitory machine-readable storage medium storing instructions which, when executed by a processing device, cause the processing device to perform operations comprising:

• receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing, wherein the data associated with the process comprises at least one of:
• an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings;
• and
• obtaining an output by applying the data associated with the process to the trained machine learning model, wherein the output is representative of the defect impact with respect to the at least one defect type.