{'fraction_non_alphanumeric': 0.017123287671232876, 'fraction_numerical': 0.0013698630136986301, 'mean_word_length': 5.2703862660944205, '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}}
Decision support system for short term statistical feedback control on a semiconductor process

Statistical process control (SPC) is a well-known method to perform quality control. However, this method is not adapted to small batches or prototype production because it usually doesn't provide feedback within a proper timeline. This paper describes a case study in which statistical based methods derived from classical SPC have been modified and implemented as part of a decision support system for a semiconductor process. Quality indexes have been defined to monitor the production, perform fault diagnosis and provide control information to operators. The system has been implemented in an electronics factory producing printed circuit board assemblies (PCBA) for telecommunication systems. With products in their prototype stage, over 40% of the production was defected requiring rework, only few data from non-consecutive measurements was available. Implementing a decision support system in such an environment was therefore a challenging task. Nevertheless, the method described in this paper provided adequate information to adjust the process when needed or to tune the system in case of false alarms. Moreover, line worker being supposed to have an active role in terms of quality; it was important to involve them in the design of the system. A greater interest from their side also improved their own understanding of the process and its problems.