Training an artificial neural network using simulated specimen images

A method, comprising:

• obtaining a first data model comprising a description of (a) one or more characteristics of a crystalline material, and (b) one or more characteristics of a first defect type of a structural defect within the crystalline material,
• wherein the first data model does not include any image data;
• applying the first data model to a simulation model to generate a first simulated specimen image which shows the crystalline material including a first set of one or more defects of the first defect type;
• inputting a set of one or more simulated specimen images as training data into a training algorithm to generate an artificial neural network, the set of simulated specimen images including the first simulated specimen image;
• generating the artificial neural network, wherein the artificial neural network is configured to detect a second set of one or more defects of the first defect type in the crystalline material as shown in a set of one or more scanned specimen images;
• identifying one or more defect identification rates for the artificial neural network that match an improvement needed criteria;
• identifying one or more correlated characteristics corresponding to the one or more defect identification rates of the artificial neural network;
• generating, based on the one or more correlated characteristics, a second simulated specimen image using the simulation model;
• and
• generating a modified artificial neural network using the second simulated specimen image,
• wherein the modified artificial neural network is configured to have improved defect identification rates for the improvement needed criteria, and
• wherein the method is performed using at least one device including a hardware processor.

A system comprising at least one device including a hardware processor, wherein the system is configured to perform operations comprising:

• obtaining a first data model comprising a description of (a) one or more characteristics of a crystalline material, and (b) one or more characteristics of a first defect type of a structural defect within the crystalline material,
• wherein the first data model does not include any image data;
• applying the first data model to a simulation model to generate a first simulated specimen image which shows the crystalline material including a first set of one or more defects of the first defect type;
• inputting a set of one or more simulated specimen images as training data into a training algorithm to generate an artificial neural network, the set of simulated specimen images including the first simulated specimen image;
• generating the artificial neural network, wherein the artificial neural network is configured to detect a second set of one or more defects of the first defect type in the crystalline material as shown in a set of one or more scanned specimen images;
• identifying one or more defect identification rates for the artificial neural network that match an improvement needed criteria;
• identifying one or more correlated characteristics corresponding to the one or more defect identification rates of the artificial neural network;
• generating, based on the one or more correlated characteristics, a second simulated specimen image using the simulation model;
• and
• generating a modified artificial neural network using the second simulated specimen image,
• wherein the modified artificial neural network is configured to have improved defect identification rates for the improvement needed criteria.