Two simple but useful methods, called the deep learning methods, for making multilayer neural networks tolerant to multiple link-weight and neuron-output faults, are proposed. The methods make the output errors in learning phase smaller than those in practical use. The abilities of fault-tolerance of the multilayer neural networks in practical use, are analyzed in the relationship between the output errors in learning phase and in practical use. The analytical result shows that the multilayer neural networks have complete (100%) fault-tolerance to multiple weight-and-neuron faults in practical use. The simulation results concerning the rate of successful learnings, the ability of fault-tolerance, and the learning time, are also shown.

A test generation method with time-expansion model can achieve high fault efficiency for acyclic sequential circuits, which can be obtained by partial scan design. This method, however, requires combinational test pattern generation algorithm that can deal with multiple stuck-at faults, even if the target faults are single stuck-at faults. In this paper, we propose a test generation method for acyclic sequential circuits with a circuit model, called MS-model, which can express multiple stuck-at faults in time-expansion model as single stuck-at faults. Our procedure can generate test sequences for acyclic sequential circuits with just combinational test pattern generation algorithm for single stuck-at faults. Experimental results show that test sequences for acyclic sequential circuits with high fault efficiency are generated in small computational effort.

In our previous paper we presented a path-tracing method of multiple gate delay fault diagnosis in combinational circuits. In this paper, we propose an improved method that uses the ambiguous delay model. This delay model makes provision for parameter variations in the manufacturing process of ICs. For the effectiveness of the current method, we propose a timed 8-valued simulation and some new diagnostic rules. Furthermore, we introduce a preparatory process that speeds up diagnosis. Also, at the end of diagnosis, additional information from the results of the preparatory process makes it possible to distinguish between non-existent faults and undiagnosed faults.

In this paper, we show two process allocation schemes to tolerate multiple faults when the primary-backup replication method is used. The first scheme, called multiple backup scheme, is running multiple backup processes for each process to tolerate multiple faults. The second scheme, called regenerative backup scheme, is running only one backup process for each process, but re-generates backup processes for processes that do not have a backup process after a fault occurrence to keep the primary-backup process pair available. In both schemes, we propose heuristic process allocation methods for balancing loads in spite of the occurrence of faults. Then we evaluate and compare the performance of the proposed heuristic process allocation methods using simulation. Next, we analyze the reliability of two schemes based on their fault-tolerance capability. For the analysis of fault-tolerance capability, we find the degree of fault tolerance for each scheme. Then we find the reliability of each scheme using Markov chains. The comparison results of two schemes indicate that the regenerative single backup process allocation scheme is more suitable than the multiple backup allocation scheme.

This paper proposes new algorithms for diagnosing (detection, identification and location) baseline multistage interconnection networks (MIN) as one of the basic units in a massively parallel system. This is accomplished in the presence of single and multiple faults under a new fault model. This model referred to as the geometric fault model, considers defective crossing connections which are located between adjacent stages, internally to the MIN (therefore, a fault corresponds to a physical bridge fault between two connections). It is shown that this type of fault affects the correct geometry of the network, thus requiring a different testing approach than previous methods. Initially, an algorithm which detects the presence of bridge faults (both in the single and multiple fault cases), is presented. For a single bridge fault, the proposed algorithm locates the fault except in an unique pathological case under which it is logically impossible to differentiate between two equivalent locations of the fault (however, the switching element affected by this fault is uniquely located). The proposed algorithm requires log2 N test vectors to diagnose the MIN as fault free (where N is the number of input lines to the MIN). For fully diagnosing a single bridge fault, this algorithm requires at most 2 log2 N tests and terminates when multiple bridge faults are detected. Subsequently, an algorithm which locates all bridge faults is given. The number of required test vectors is O(N). Fault location of each bridge fault is accomplished in terms of the two lines in the bridge and the numbers of the stages between which it occurs. Illustrative examples are given.

This paper presents a system that automatically refines the theory expressed in the function-free first-order logic. Our system can efficiently correct multiple faults in both the concept and subconcepts of the theory, given only the classified examples of the concept. It can refine larger classes of theory than existing systems can since it has overcome many of their limitations. Our system is based on a new combination of an inductive and an explanation-based learning algorithms, which we call the biggest-first multiple-example EBL (BM-EBL). From a learning perspective, our system is an improvement over the FOIL learning system in that our system can accept a theory as well as examples. An experiment shows that when our system is given a theory that has the classification error rate as high as 50%, it can still learn faster and with more accuracy than when it is not given any theory.

The single fault model is invalid in many cases. However, it is very difficult to generate tests for all multiple faults since an m-line circuit may have 3m --1 multiple faults. In this paper, we describe a method for generating tests for combinational circuits with multiple stuck-at faults. An input vector is a test for a fault on a target line, if it find the target line to be fault-free in the presence of undetected or undetectable lines. The test is called a robust test for fault on a target line. It is shown that the sensitizing input-pair for a completely single sensitized path can be a robust test-pair. The method described here consists of two procedures. We label these as SINGLE_SEN" procedure and DECISION" procedure. SINGLE_SEN generates a single sensitized path including a target line on it by using a PODEM-like method which uses a new seven-valued calculus. DECISION determines by utilizing the method proposed by H. Cox and J. Rajski whether the single sensitizing input-pair generated by the SINGLE_SEN is a robust test-pair. By using these two procedures the described method generates robust test-pairs for the combinational circuit with multiple stuck-at faults. Finally, we demonstrate by experimental results on the ISCAS85 benchmark circuits that SINGLE_SEN is effective for an algorithmic multiple fault test generation for circuits not including many XOR gates.