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.

Parity-Based Fault Detection Architecture of S-box for Advanced Encryption Standard

Abstract: In this paper, the authors present parity-based fault detection architecture of the S-box for designing high performance fault detection structures of the advanced encryption standard. Instead of using look-up tables for the S-box and its parity prediction, logical gate implementations based on the composite field are utilized. After analyzing the error propagation for injected single faults, the authors modify the original S-box and suggest fault detection architecture for the S-box. Using the closed formulations for the predicted parity bits, the authors propose a parity-based fault detection scheme for reaching the maximum fault coverage. Moreover, the overhead costs, including space complexity and time delay of our modified S-box and the parity predictions are also compared to those of the previously reported ones

Evaluation of the quality of N-detect scan ATPG patterns on a processor

Abstract: This paper evaluates N-detect scan ATPG patterns for their impact to test quality through simulation and fallout from production on a Pentium 4 processor using 90 nm manufacturing technology. An incremental ATPG flow is used to generate N-detect test patterns. The generated patterns were applied in production with flows to determine overlap in fallout to different tests. The generated N-detect test patterns are then evaluated based on different metrics. The metrics include signal states, bridge fault coverage, stuck-at fault coverage and fault detection profile. The correlation between the different metrics is studied. Data from production fallout shows the effectiveness of N-detect tests. Further, the correlation between fallout data and the different metrics is analyzed.

On the application of symbolic techniques to the multiple fault location in low testability analog circuits

Abstract: A new approach for the multiple fault location in linear analog circuits is proposed. It presents the characteristic of using classical numerical procedures together with symbolic analysis techniques, which is particularly useful in the parametric fault diagnosis field. The proposed approach is based on the k-fault hypothesis and is provided with efficient algorithms for fault location also in the case of low testability circuits. The developed algorithms have been used for realizing a software package prototype which implements a fully automated system for the fault location in linear analog circuits of moderate size.

Fault diagnosis in power transformers using multi-class logical analysis of data

Abstract: This paper presents the implementation of a novel multi-class diagnostic technique for the detection and identification of faults based on an approach called logical analysis of data (LAD). LAD is a data mining, artificial intelligence approach that is based on pattern recognition. In the context of condition based maintenance (CBM), historical data containing condition indices and the state of the machine are the inputs to LAD. After training and testing phases, LAD generates patterns that characterize the faulty states according to the type of fault, and differentiate between these states and the normal state. These patterns are found by solving a mixed 0---1 integer linear programming problem. They are then used to detect and to identify a future unknown state of equipment. The diagnostic technique has already been tested on several known machine learning datasets. The results proved that the performance of this technique is comparable to other conventional approaches, such as neural network and support vector machine, with the added advantage of the clear interpretability of the generated patterns, which are rules characterizing the faults' types. To demonstrate its merit in fault diagnosis, the technique is used in the detection and identification of faults in power transformers using dissolved gas analysis data. The paper reaches the conclusion that the multi-class LAD based fault detection and identification is a promising diagnostic approach in CBM.