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With the rapid growth in the semiconductor industry, it is becoming criticalto detect and classify increasingly smaller patterned defects. Recently machinelearning, including deep learning, has come to aid in this endeavor in a bigway. However, the literature shows that it is challenging to successfullyclassify defect types at the 6 nm node with 100% accuracy using low-cost andhigh-volume-manufacturing compatible optical imaging methods. Here we combine aconvolutional neural network (CNN) with that of an optical imaging methodcalled through-focus scanning optical microscopy (TSOM) to successfullyclassify patterned defects for the 6 nm node targets using simulated opticalimages at the 193 nm illumination wavelength. We demonstrate the successfulclassification of eight variations of the defects, including the 3 nmdifference in the defect size in one dimension, which is over 50 times smallerthan the illumination wavelength used.

Application of Convolutional Neural Network to TSOM Images for Classification of 6 nm Node Patterned Defects