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An estimate of a measurand using a nonlinear function of uncorrelated input quantities can be done by either applying the nonlinear function to the means of the input quantities (Method 1) or calculating the mean of a set of values obtained from propagating individual measurement values through the nonlinear function (Method 2). This paper proposes an improvement over the standard Method 2 procedures when the input quantities are assumed to be statistically independent and the nonlinear function has a general sum-of-product form, which covers many common measurement models. This paper shows that the proposed new approach (called Method 2S), if applicable, always produces a mean-squared error smaller than that of the conventional Method 2 procedures. The proposed approach improves the efficiency of Type-A evaluation as well as the Monte Carlo method. It also well complements the mainstream practices in the measurement uncertainty evaluation. [ABSTRACT FROM AUTHOR]

Expanded uncertainty estimation is normally required for mission-critical applications, e.g., those involving health and safety. It helps to get a distribution range of the required confidence level for the uncertainty evaluation of a system. There are a number of available techniques to estimate the expanded uncertainty. However, there is currently no commonly accepted benchmark test distribution set adopted to compare the performances of different techniques when they are used to estimate the expanded uncertainty. Without such a common benchmarking platform, the relative reliability of a particular technique in comparison to other techniques can be untrustworthy. To address the shortcoming, this paper proposes a set of analytically derived benchmark test distributions. It goes on to show the benefits of using them by comparing the performance of existing distribution fitting techniques when applied to the moment-based expanded uncertainty evaluation. The most commonly used moment-based distribution fitting techniques, such as Pearson, Tukey’s gh, Cornish–Fisher expansion, and extended generalized lambda distributions, are employed as test cases in this paper. The test distribution set proposed in this paper provides a common benchmarking platform for metrologists intending to assess the performance of different expanded uncertainty estimation techniques. Results from the performance comparison would help practitioners to make a better choice of a distribution fitting technique that would best suit their respective systems. [ABSTRACT FROM AUTHOR]

A multisine excitation signal enables fast and statistically robust measurement of the nonlinear behavior of analog devices under test. This paper describes the design considerations and constraints of a multisine excitation signal when it is applied to analog-to-digital converter testing. A novel phase-plane uniformity objective function followed by an optimization procedure is proposed to optimize the multisine excitation signal. Performance comparison between the proposed multisine design and other alternative multisine design is carried out. It is demonstrated that the proposed phase-space uniform multisine enables better representation of the conversion error distribution compared with existing multisine designs. This results in a more accurate assessment of the analog-to-digital converter performance. [ABSTRACT FROM PUBLISHER]

An automated frequency selection algorithm for dual-tone analog-to-digital converter sine-wave test that guarantees optimal test coverage in the phase-plane is presented. The proposed method relaxes some constraints of the existing phase-space design methodology. This enables wider application of the phase-space method in test frequency selection. The proposed algorithm also extends the previous works by generating a good frequency pair while taking into account various hardware constraints in the production environment. Quality measures for phase-plane coverage is proposed and evaluated, followed by the theoretical analysis and description of the phase-plane uniformity. Extensive computer simulation is carried out to validate the proposed technique. [ABSTRACT FROM AUTHOR]

High-volume production data shows that dies, which failed probe test on a semiconductor wafer, have a tendency to form certain unique patterns, i.e., defect clusters. Identifying such clusters is one of the crucial steps toward improvement of the fabrication process and design for manufacturing. This paper proposes a new technique for defect-cluster identification that combines data mining with a defect-cluster extraction using a Segmentation, Detection, and Cluster-Extraction algorithm. It offers high defect-extraction accuracy, without any significant increase in test time and cost. [ABSTRACT FROM PUBLISHER]