Power supply partitioning for placement of built-in current sensors for IDDQ testing

IDDQ testing has been a very useful test screen for CMOS circuits. However, with each technology node the background leakage of chips is rapidly increasing. As a result it is becoming more difficult to distinguish between faulty and fault-free chips using IDDQ testing. Power supply partitioning has been proposed to increase test resolution by partitioning the power supply network, such that each partition has a relatively small defect-free IDDQ level. However, at present no practical partitioning strategy is available. The contribution of this thesis is to present a practical power supply partitioning strategy. We formulate various versions of the power supply partitioning problem that are likely to be of interest depending on the constraints of the chip design. Solutions to all the variants of the problem are presented. The basic idea behind all solutions is to abstract the power topology of the chip as a flow network. We then use flow techniques to find the min-cut of the transformed network to get solutions to our various problem formulations. Experimental results for benchmark circuits verify the feasibility of our solution methodology. The problem formulations will give complete flexibility to a test engineer to decide which factors cannot be compromised (e.g. area of BICS, test quality, etc) for a particular design and accordingly choose the appropriate problem formulation. The application of this work will be the first step in the placement of Built-In Current Sensors for IDDQ testing

Variance reduction and outlier identification for IDDQ testing of integrated chips using principal component analysis

Integrated circuits manufactured in current technology consist of millions of transistors with dimensions shrinking into the nanometer range. These small transistors have quiescent (leakage) currents that are increasingly sensitive to process variations, which have increased the variation in good-chip quiescent current and consequently reduced the effectiveness of IDDQ testing. This research proposes the use of a multivariate statistical technique known as principal component analysis for the purpose of variance reduction. Outlier analysis is applied to the reduced leakage current values as well as the good chip leakage current estimate, to identify defective chips. The proposed idea is evaluated using IDDQ values from multiple wafers of an industrial chip fabricated in 130 nm technology. It is shown that the proposed method achieves significant variance reduction and identifies many outliers that escape identification by other established techniques. For example, it identifies many of the absolute outliers in bad neighborhoods, which are not detected by Nearest Neighbor Residual and Nearest Current Ratio. It also identifies many of the spatial outliers that pass when using Current Ratio. The proposed method also identifies both active and passive defects

Integrated circuit outlier identification by multiple parameter correlation

Semiconductor manufacturers must ensure that chips conform to their specifications before they are shipped to customers. This is achieved by testing various parameters of a chip to determine whether it is defective or not. Separating defective chips from fault-free ones is relatively straightforward for functional or other Boolean tests that produce a go/no-go type of result. However, making this distinction is extremely challenging for parametric tests. Owing to continuous distributions of parameters, any pass/fail threshold results in yield loss and/or test escapes. The continuous advances in process technology, increased process variations and inaccurate fault models all make this even worse. The pass/fail thresholds for such tests are usually set using prior experience or by a combination of visual inspection and engineering judgment. Many chips have parameters that exceed certain thresholds but pass Boolean tests. Owing to the imperfect nature of tests, to determine whether these chips (called "outliers") are indeed defective is nontrivial. To avoid wasted investment in packaging or further testing it is important to screen defective chips early in a test flow. Moreover, if seemingly strange behavior of outlier chips can be explained with the help of certain process parameters or by correlating additional test data, such chips can be retained in the test flow before they are proved to be fatally flawed. In this research, we investigate several methods to identify true outliers (defective chips, or chips that lead to functional failure) from apparent outliers (seemingly defective, but fault-free chips). The outlier identification methods in this research primarily rely on wafer-level spatial correlation, but also use additional test parameters. These methods are evaluated and validated using industrial test data. The potential of these methods to reduce burn-in is discussed