Stuck-at fault

About: Stuck-at fault is a research topic. Over the lifetime, 9707 publications have been published within this topic receiving 160254 citations.

Applying a robust heteroscedastic probabilistic neural network to analog fault detection and classification

Abstract: The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques such as neural networks have been employed to automate classification. The major drawback to such techniques has been the implicit assumption that the variances of the responses of faulty circuits have been the same as each other and the same as that of the fault-free circuit. This assumption can be shown to be false. Neural networks, moreover, have proved to be slow. This paper describes a new neural network structure that clusters responses assuming different means and variances. Sophisticated statistical techniques are employed to handle situations where the variance tends to zero, such as happens with a fault that causes a response to be stuck at a supply rail. Two example circuits are used to show that this technique is significantly more accurate than other classification methods. A set of responses can be classified in the order of 1 s.

Analyzing the effectiveness of multiple-detect test sets

Abstract: Multiple-detect test sets have been shown to be effective in lowering defect level. Other researchers have noted that observing the effects of a defect can be controlled by sensitizing affected sites to circuit outputs but defect excitation is inherently probabilistic given a defect’s inherent, unknown nature. As a result, test sets that sensitize every signal line multiple times with varying circuit state has a greater probability of detecting a defect. In past work, the entire circuit is considered when varying circuit state from one vector to another for a given signal line. However, it may be possible to improve defect excitation by exploiting the localized nature of many defect types. Spec$cally, by varying circuit state in the physical region or neighborhood surrounding a line affected by a defect, the defect excitation and therefore detection can be improved. In this paper, we present a method for extracting a physical region surrounding a signal line but more importantly, techniques for analyzing the excitation characteristics of the region. Analysis of 4-detect test sets reveals that 30% to 60% of signal line regions do not achieve at least four unique states, indicating opportunity to further reduce defect level.

New techniques for accelerating fault injection in VHDL descriptions

Abstract: Simulation-based fault injection in VHDL descriptions is increasingly common due to the popularity of top-down design flows exploiting this language. However, the large CPU time required to perform VHDL simulations often represents a major drawback stemming from the adoption of this method. This paper presents some techniques for reducing the time to perform the fault injection experiments. Static and dynamic methods are proposed to analyze the list of faults to be injected, and for removing faults as soon as their behaviour is known. Common features available in most VHDL simulation environments are also exploited. Experimental results show that the proposed techniques are able to reduce the time required by a typical fault injection campaign by a factor ranging from 51% to 96%.

Improved fault location algorithm for multi-location faults, transforming faults and shunt faults in thyristor controlled series capacitor compensated transmission line

Abstract: Fault location estimation in series compensated transmission lines is quite difficult because a non-linear current dependent circuit appears between the substation and fault point. In particular, the faults which occur at different locations at the same time in different phases known as multi-location faults has not been addressed by researchers. Other types of fault that may occur in transmission lines are transforming faults where one type of fault transforms to another type fault after some time. In this study, a fault location estimation scheme using artificial neural network (ANN) is presented for multi-location faults, transforming faults as well as for commonly occurring shunt faults in thyristor controlled series capacitor (TCSC) compensated transmission line. DB-4 wavelet is used for pre-processing of the three-phase current and voltage signals. The shunt capacitance of the line is considered based on distributed parameter line model. Feasibility of the ANN-based fault location algorithm is tested under a wide variation of parameters, such as fault type, location, fault resistance and fault inception angle. Fault location errors are within 0.001–1% range.

Model-Based Health Monitoring for a Vehicle Steering System With Multiple Faults of Unknown Types

Abstract: This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool Multiple faults of different types, ie, abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs) A fault discriminator is constructed to distinguish the type of faults once they are detected After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle