Defects such as cracks or pinholes introduced during film formation or due to physical manipulation of nanometer-thick organic semiconductor (OSC) films can detrimentally impact device performance or lead to device failure. Microscopic defects are often evaluated manually and are subjected to biasness by the operator. This project involves collecting optical microscope images and using artificial intelligence to auto-identify and classify various types of defects on OSC films based on these images.