{'fraction_non_alphanumeric': 0.014822134387351778, 'fraction_numerical': 0.0029644268774703555, 'mean_word_length': 5.798657718120805, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An automated mask defect analysis system for increasing mask shop productivity

The detection, classification and disposition of defects is an important function that commands significant resources in mask making. Current processes use manual evaluation of defects, which is slow, subject to errors, and provides sparse data for process improvement. The automated defect analysis software described here reads inspection reports from mask inspection tools, classifies each defect, and measures both its size and printability. It combines and compares data from multiple inspections to provide critical process development data. Data from 144 masks is presented showing that the system missed no critical defects found by operators. These inspections also demonstrated numerous occasions for improved classifications compared to that given by the operators. This capability gives improved disposition, an easy path to using simulator based printability for disposition, and significant improvements in mask yield.