Error Cause Analysis of Laboratory Results with the Help of AI

In the electronics laboratory, a large amount of different devices has to be tested, and the characterization of each of them generates a large amount of data. Classical root cause analysis is inherently inefficient because it requires manual inspection by experts, turning out to be costly and time consuming. Furthermore, automatic evaluations through sequences of conditions for the signals, ending up in hard-coded logical formulas, are still inappropriate due to still the necessity of experts, in order to design them specifically for each different device, and to the complexity of the data itself, which may lead to the infeasibility of such approach. For these reasons, in this thesis, first steps towards a machine learning (ML) approach are investigated, laying the foundation into the transition to a ML root cause analysis approach.