Open faults are difficult to test since the voltage at the floating line is unpredictable and depends on the voltage at the adjacent lines. The effect of open faults can be easily excited if a test pattern provides the opposite logic value to most of the adjacent lines. In this paper, we present a procedure to generate as high a quality test as possible. We define the test quality for evaluating the effect of adjacent lines by assigning an opposite logic value to the faulty line. In our proposed test generation method, we utilize the SAT-based ATPG method. We generate test patterns that propagate the faulty effect to primary outputs and assign logic values to adjacent lines opposite that of the faulty line. In order to estimate test quality for open faults, we define the excitation effectiveness Eeff. To reduce the test volume, we utilize the open fault simulation. We calculate the excitation effectiveness by open fault simulation in order to eliminate unnecessary test patterns. The experimental results for the benchmark circuits prove the effectiveness of our procedure.

Physical defects that are not covered by stuck-at fault or bridging fault model are increasing in LSI circuits designed and manufactured in modern Deep Sub-Micron (DSM) technologies. Therefore, it is necessary to target non-stuck-at and non-bridging faults. A stuck-open is one such fault model that captures transistor level defects. This paper presents two methods for maximizing stuck-open fault coverage using stuck-at test vectors. In this paper we assume that a test set to detect stuck-at faults is given and we consider two formulations for maximizing stuck-open coverage using the given test set as follows. The first problem is to form a test sequence by using each test vector multiple times, if needed, as long as the stuck-open coverage is increased. In this case the target is to make the resultant test sequence as short as possible under the constraint that the maximum stuck-open coverage is achieved using the given test set. The second problem is to form a test sequence by using each test vector exactly once only. Thus in this case the length of the test sequence is maintained as the number of given test vectors. In both formulations the stuck-at fault coverage does not change. The effectiveness of the proposed methods is established by experimental results for benchmark circuits.

A method for detecting interconnect open faults of CMOS combinational circuits by applying a ramp voltage to the power supply terminal is proposed. The method can assign a known logic value to a fault location automatically by applying a ramp voltage and as a result, it requires only one test vector to detect a fault as a delay fault or an erroneous logic value at primary outputs. In this paper, we show fault detectability and effectiveness of the proposed method by simulation-based and theoretical analysis. We also expose that the method can be applicable to every fault location in a circuit and open faults with any value. Finally, we show ATPG results that are suitable to the proposed method.

A new learning algorithm is proposed to enhance fault tolerance ability of the feedforward neural networks. The algorithm focuses on the links (weights) that may cause errors at the output when they are open faults. The relevances of the synaptic weights to the output error (i.e. the sensitivity of the output error to the weight fault) are estimated in each training cycle of the standard backpropagation using the Taylor expansion of the output around fault-free weights. Then the weight giving the maximum relevance is decreased. The approach taken by the algorithm described in this paper is to prevent the weights from having large relevances. The simulation results indicate that the network trained with the proposed algorithm do have significantly better fault tolerance than the network trained with the standard backpropagation algorithm. The simulation results show that the fault tolerance and the generalization abilities are improved.