Identification of hot spots or defects by machine learning

A method comprising:

• obtaining a plurality of sets of one or more characteristics of performance of a plurality of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process;
• determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective;
• obtaining a characteristic of each of the process conditions;
• obtaining a characteristic of each of the hot spots;
• and
• training, by a hardware computer system, a machine learning model using a training set comprising a plurality of samples, wherein each of the samples has a feature vector comprising the respective characteristic of one of the process conditions and the respective characteristic of one of the hot spots, the feature vector further comprising a label comprising whether that hot spot is defective under that process condition.

A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:

• obtain a plurality of sets of one or more characteristics of performance of a hot spot under a plurality of process conditions in a device manufacturing process, respectively;
• determine, for each of the process conditions, based on the set of one or more characteristics under that process condition, whether the hot spot is defective;
• obtain a characteristic of each of the process conditions;
• and
• train a machine learning model using a training set comprising a plurality of samples, wherein each of the samples has a feature vector comprising the respective characteristic of one of the process conditions and a label comprising whether the hot spot is defective under that process condition.

A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:

• obtain a plurality of sets of one or more characteristics of performance of a plurality of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process;
• determine, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective;
• obtain a characteristic of each of the process conditions;
• obtain a characteristic of each of the hot spots;
• and
• train a machine learning model using a training set comprising a plurality of samples, wherein each of the samples has a feature vector comprising the respective characteristic of one of the process conditions and the respective characteristic of one of the hot spots, the feature vector further comprising a label comprising whether that hot spot is defective under that process condition.