An electrical current flow technology for the next generation of automated testing equipment

This paper presents a technology to measure and analyze electrical current (EC) flow in units-under-test (UUT). The paper discusses the results of preliminary investigations and high-level design specifications that will lead to the development of a prototype to demonstrate the concepts and the benefits of using EC data and information. Non-intrusive and intrusive sensor technology, and experimental results are presented. The characteristics and benefits of each sensor and technology are discussed. EC measurements are characterized by collection and domain types, and include time-domain signatures and single/multiple signals, and frequency-domain power spectral density (PSD) functions. EC data is then applied to neural network (NN) algorithms to provide pass/fail test results. The paper discusses NN methods of learning and processing for the different classes of signals. The methods and technologies discussed here introduce a valuable capability for test equipment operators, repair technicians, and test software engineers in the next generation of automated testing equipment (ATE).