Size Of Fault Dictionary Research Articles

A Novel Test Point Selection Method for Analog Fault Dictionary Techniques

Most of the recently reported test point selection algorithms for analog fault dictionary techniques are based on integer-coded table (ICT) technique. Hence, the accuracy of these algorithms is closely related to the accuracy of the ICT technique. Unfortunately, this technique is not accurate, especially when the size of fault dictionary is large. This paper proposes an accurate fault-pair Boolean table technique for the test point selection problem. First, the approach to transform the fault dictionary into a fault-pair Boolean table is introduced. Then, a test point selection algorithm based on the fault-pair Boolean table is proposed. Thirdly, several example circuits are used to illustrate the proposed algorithm. Simulated results indicate that the proposed method is more accurate than the other methods. Therefore, it is a good solution for minimizing the size of the test point set.

Fault dictionary based switched current circuit fault diagnosis using entropy as a preprocessor

A novel method based on a fault dictionary that uses entropy as a preprocessor to diagnose faulty behavior in switched current (SI) circuit is presented in the paper. The proposed method uses a data acquisition board to extract the original signal form the output terminals of the circuit-under-tests. These original data are fed to the preprocessors for feature extraction and finds out the entropies of the signals which are a quantitative measure of the information contained in the signals. The proposed method has the capability to detect and identify faulty transistors in SI circuit by analyzing its output signals with high accuracy. Using entropy of signals to preprocess the circuit response drastically reduces the size of fault dictionary, minimizing fault detect time and simplifying fault dictionary architecture. The result from our examples showed that entropies of the signals fall on different range when the faulty transistors` Transconductance Gm value varying within their tolerances of 5 or 10%, thus we can identify the faulty transistors correctly when the response do not overlap. The average accuracy of fault recognition achieved is more than 95% although there are some overlapping data when tolerance is considered. The method can classify not only parametric faults but also catastrophic faults. It is applicable to analog circuits as well as SI ones. A low-pass and a band-pass SI filter and a Clock feedthrough cancellation circuit have been used as test beached to verify the effectiveness of the proposed method. A comparison of our work with Yuan et al. (IEEE Trans Instrum Meas 59(3):586---595, 2010), which used entropy and kurtosis as preprocessors, reveals that our method requiring one feature parameter reduces the computation and fault diagnosis time.

Reducing fault dictionary size for million-gate large circuits

In general, fault dictionary is prevented from practical applications in fault diagnosis due to its extremely large size. Several previous works are proposed for the fault dictionary size reduction. However, some of them fail to bring down the size to an acceptable level, and others might not be able to handle today's million-gate circuits due to their high time and space complexity. In this article, an algorithm is presented to reduce the size of pass-fail dictionary while still preserving high diagnostic resolution. The proposed algorithm possesses low time and space complexity by avoiding constructing the huge distinguishability table, which inevitably boosts up the required computation complexity. Experimental results demonstrate that the proposed algorithm is capable of handling industrial million-gate large circuits in a reasonable amount of runtime and memory.