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A novel approach for estimating variation in the TDDB failure time is reported. The results for structures with Dual Damascene copper-based metallization and a low-k dielectric material demonstrate that variation in the initial current at stress reasonably predicts variation in the TDDB failure time. Moreover, the method does not cause failure in the structures and is more efficient compared to other...
This paper presents two improved models based on the first-order multi-variable grey model (GM(1, N)) for forecasting the electricity demand. The first model named IGM1(1, N) is developed through the optimization of background value by Lagrange mean value theorem (LMVT). Another model named IGM2(1, N) is established through the calculation of its boundary value using least square method (LSM). Despite...
In this paper, a correlation between the I-V slope at low fields and TDDB voltage acceleration is demonstrated for the first time, based on a wide range of data from 32 nm to 130 nm node hardware. The data supports the radicE model, which is based on electron fluence (leakage current) driven, Cu catalyzed, low-k dielectric breakdown. Using this correlation, a fast wafer level screen method was also...
Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are noninvasive neuroimaging technologies providing functional mapping of stimulus activated voxels and detailed connectivity structures in the brain, which are traditionally based on simplified linear models. Despite the unique functional and structural representations achievable by fMRI and DTI, respectively, both representations...
Feature extraction has been widely used in sensor fault detection. Commonly used feature extraction methods such as PCA and MDS involve signal process of liner time-invariant systems, which are less effective in dealing with the nonlinear systems. In this paper, we will present that Local Linear Embedding (LLE) concept is adopted to solve the fault detection problems and that certain enhancement have...
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