An investigation of defect detection using random defect excitation and deterministic defect observation in complex integrated logic circuits

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references: leaves 22-23.aWhenever integrated circuits are manufactured, a certain percentage of those circuits will be defective. Defective circuits present problems for both the manufacturers who wish to maintain a good reputation with their customers and the consumers who depend upon the correct operation of the products they buy. Thus, testing must be done to detect which parts are defective so that they are not sold to unwitting consumers. Most current testing methods involve generating test patterns that will detect single stuck-at faults. Unfortunately, however, the single stuck-at fault model cannot adequately describe all of the potential defects that may occur. The requirements for exciting a fault vary depending upon the specific model (stuck-at, bridge, etc.) being used, but the observation of the fault always requires that the erroneous logic value be propagated to a primary output. The proposed new method of automatic test pattern generation involves deterministically observing all of the sites in the circuit as many times as possible while randomly exciting the defects which may occur. This research demonstrates the importance of site observation on the detection of defects and shows some of the inefficiencies and shortcomings of the current stuck-at fault ATPG

Estimating the expected latency to failure due to manufacturing defects

Manufacturers of digital circuits test their products to find defective parts so they are not sold to customers. Despite extensive testing, some of their products that are defective pass the testing process. To combat this problem, manufacturers have developed a metric called defective part level. This metric measures the percentage of parts that passed the testing that are actually defective. While this is useful for the manufacturer, the customer would like to know how long it will take for a manufacturing defect to affect circuit operation. In order for a defect to be detected during circuit operation, it must be excited and observed at the same time. This research shows the correlation between defect detection during automatic test pattern generation (ATPG) testing and normal operation for both combinational and sequential circuits. This information is then used to formulate a mathematical model to predict the expected latency to failure due to manufacturing defects

Modeling defective part level due to static and dynamic defects based upon site observation and excitation balance

Manufacture testing of digital integrated circuits is essential for high quality. However, exhaustive testing is impractical, and only a small subset of all possible test patterns (or test pattern pairs) may be applied. Thus, it is crucial to choose a subset that detects a high percentage of the defective parts and produces a low defective part level. Historically, test pattern generation has often been seen as a deterministic endeavor. Test sets are generated to deterministically ensure that a large percentage of the targeted faults are detected. However, many real defects do not behave like these faults, and a test set that detects them all may still miss many defects. Unfortunately, modeling all possible defects as faults is impractical. Thus, it is important to fortuitously detect unmodeled defects using high quality test sets. To maximize fortuitous detection, we do not assume a high correlation between faults and actual defects. Instead, we look at the common requirements for all defect detection. We deterministically maximize the observations of the leastobserved sites while randomly exciting the defects that may be present. The resulting decrease in defective part level is estimated using the MPGD model. This dissertation describes the MPGD defective part level model and shows how it can be used to predict defective part levels resulting from static defect detection. Unlike many other predictors, its predictions are a function of site observations, not fault coverage, and thus it is generally more accurate at high fault coverages. Furthermore, its components model the physical realities of site observation and defect excitation, and thus it can be used to give insight into better test generation strategies. Next, we investigate the effect of additional constraints on the fortuitous detection of defects-specifically, as we focus on detecting dynamic defects instead of static ones. We show that the quality of the randomness of excitation becomes increasingly important as defect complexity increases. We introduce a new metric, called excitation balance, to estimate the quality of the excitation, and we show how excitation balance relates to the constant τ in the MPGD model