Current EDA market has plenty of DFM (Design for Manufacturing) solutions on via doubling for VLSI design which enhances single-level metal (hierarchy) interconnections. A new conceptual approach, ...Multi-Level Metal and Via (MLMV) is proposed to extend the capability to insert metals and vias across multiple hierarchies to lower effective resistance. The objective is to improve signal integrity by reducing resistance across metal paths for individual signals, inclusive of supplies across the full chip. MLMV also takes into consideration the critical signals integrity of the design. The tool ensures no metal insertion is too close to critical signals, to prevent potential noise in the design. The results discussed in this paper show a significant improvement in terms of reducing the effective resistance of experimental test case signal path up to 90% in comparing to the conventional via filling solution. With these significant results, it can be concluded that MLMV is able to populate the metal and via effectively and minimizing resistance in the design.
To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study ...with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005-2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.
CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ(42). Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.
The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD.
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