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In order to reduce the number of defective parts and increase yield, especially in early stages of production, systematic defects must be identified and corrected as soon as possible. This paper presents a technique to move defect classification to the earliest phase of volume testing without any special diagnostic test patterns. A neural-network-based fault classifier is described, which is able...
This paper presents a method to evaluate the impact of process and environmental variations on the overall performance of biologically inspired spiking neural networks, implemented predominantly ind digital CMOS. In this method, transistor-level and behavioral level analysis are carried out. Then, the results of the transistor-level simulation are projected on the application layer to determine the...
A multiple faults test generation algorithm based neural networks for digital circuits is proposed in this paper because the test generation for multiple faults in digital circuits is more difficult. This algorithm change multiple faults into single fault firstly and constructs the constraint network of the fault for the single fault circuit with method of neural networks. The test vectors for multiple...
Online testing, one of the most challenging issues in design for test domain, is intended for inspection of digital systems behavior during their working period. This paper presents a novel approach for simultaneous online testing of several combinational circuits using a reconfigurable neural network implemented along the original hardware. Automatic generation of the neural network to model the...
A method of a global parametric faults diagnosis in analogue integrated circuits is presented in this paper. The method is based on basic features calculated from a circuit's under test time domain response to a voltage step, i.e. locations of maxima and minima of circuit under test response and its first order derivative. The testing and diagnosis process is executed with the use of an artificial...
Combining the failure practical example, neural network analysis is used for diagnosis. Using the neural network in MATLAB to simulate the circuit fault system, this is using two different kinds of training functions. For the better analysis of failure diagnosis problems, firstly, using MATLAB software to simulate failure problems such as the emergence of regular power, short circuit and broken circuit...
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