Machine Learning and Data Mining Methods in Testing and Diagnostics of Analog and Mixed-Signal Integrated Circuits: Case Study
Artificial intelligence methods are widely used in different interdisciplinary areas. The paper is devoted to application the method of machine learning and data mining to construction a neuromorphic fault dictionary (NFD) for testing and fault diagnostics in analog/mixed-signal integrated circuits. The main issues of constructing a NFD from the big data point of view are considered. The method of reducing a set of essential characteristics based on the principal component analysis and approach to a cut down the training set using entropy estimation are proposed. The metrics used for estimating the classification quality are specified based on the confusion matrix. The case study results for analog filters are demonstrated and discussed. Experimental results for both cases demonstrate the essential reduction of initial training set and saving of time on the NFD training with high fault coverage up to 100%. The proposed method and approach can be used according to the design-for-testability flow for analog/mixed-signal integrated circuits.
KeywordsMachine learning Data mining Testing Diagnostics Analog and mixed-signal IC Entropy Principal component analysis Fault coverage Neuromorphic fault dictionary
The work is performed according to the Russian Government Program of Competitive Growth of Kazan Federal University.
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