Certain investigation on parametric fault diagnosis for analog circuits using machine learning techniques

Abstract

Analog electronic circuit fault diagnosis has gained wide spread attention in the area of Very Large Scale Integration VLSI testing with the help of computers Miniaturization of modern electronic devices leads to more complex circuits and systems The Application Specific Integrated Circuits ASIC custom-made for a particular task or the System on Chip SoC integrated all the components into one chip containing analog digital and mixed signal parts Analog and Mixed Signal AMS Integrated Circuits ICs are acquiring popularity in several applications such as customer electronics biomedical equipments wireless communications networking multimedia automotive process control and real-time control system AMS ICs makes up the bulk of future devices and hence it is necessary to perform research in AMS testing Fault diagnosis in analog electronic circuits is an intricate problem due to a limited number of outputs inputs and test signals Development of models for diagnosing faults is difficult due to complex nonlinear dependence between fault types and distinctiveness of the testing signals The complexity of testing of analog circuits are due to the changes in the technological process the growing scale of integration the rise of functional complexity absence of access to internal components and nodes of the circuit etc Due to the component value variation beyond the tolerance limit in analog circuits there exists an infinite number of good machines but in the digital domain there is only one good circuit. newline