Binning for IC Quality: Experimental Studies on the SEMATECH Data

The earlier smaller bipolar study did not provide a high enough bin 0 population to directly observe test escapes and thereby estimate defect levels for the best bin. Results presented here indicate that the best bin can be reasonably expected to show a 2 - 5 factor improvement in defect levels over the average for the lot for moderate to high yields (the overall yield for these experiments was approximately 65%). The experiments also confirm the dependence of the best bin quality on test transparency. The defect level improvement is poorer for the case Of IDDQ escapes where the tests applied had a much higher escape rate. Overall experimental results are consistent with analytical projections for typical values of the clustering parameter in [9]. The final version of this paper will include extensive analysis to validate the analytical models based on this data

A Novel Method to Improve the Test Efficiency of VLSI Tests

This paper considers reducing the cost of test application by permuting test vectors to improve their defect coverage. Algorithms for test reordering are developed with the goal of minimizing the test cost. Best and worst case bounds are established for the performance of a reordered sequence compared to the original sequence of test application. SEMATECH test data and simulation results are used throughout to illustrate the ideas

Characterizing the LSI Yield Equation from Wafer Test Data

The results of production test on LSI wafers are analyzed to determine the parameters of the yield equation. Recognizing that a physical defect on a chip can produce several logical faults, the number of faults per defect is assumed to be a random variable with Poisson distribution. The analysis provides a relationship between the yield of the tested fraction of -the chip area and the cumulative fault coverage of test patterns. The parameters of the yield equation are estimated by fitting this relation to the measured yield versus fault coverage data

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