3D DRAM is the next-generation memory system targeting high bandwidth, low power, and small form factor. This paper presents a cross-domain CAD/architectural platform that addresses DC power noise issues in 3D DRAM targeting stacked DDR3, Wide I/O, and hybrid memory cube technologies. Our design and analysis include both individual DRAM dies and a host logic die that communicates with them in the same stack. Moreover, our comprehensive solutions encompass all major factors in design, packaging, and architecture domains, including power delivery network wire sizing, redistribution layer routing, distributed, and dedicated TSV placement, die bonding style, backside wire bonding, and read policy optimization. We conduct regression analysis and optimization to obtain high quality solutions under noise, cost, and performance tradeoff. Compared with industry standard baseline designs and policies, our methods achieve up to 68.2% IR-drop reduction and 30.6% performance enhancement.

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Runoff behaviors by five bias correction methods were analyzed, which were Change Factor methods using past observed and estimated data by the estimation scenario with average annual calibration factor (CF_Y) or with average monthly calibration factor (CF_M), Quantile Mapping methods using past observed and estimated data considering cumulative distribution function for entire estimated data period (QM_E) or for dry and rainy season (QM_P), and Integrated method of CF_M+QM_E(CQ). The peak flow by CF_M and QM_P were twice as large as the measured peak flow, it was concluded that QM_P method has large uncertainty in monthly runoff estimation since the maximum precipitation by QM_P provided much difference to the other methods. The CQ method provided the precipitation amount, distribution, and frequency of the smallest differences to the observed data, compared to the other four methods. And the CQ method provided the rainfall-runoff behavior corresponding to the carbon dioxide emission scenario of SRES A1B. Climate change scenario with bias correction still contained uncertainty in accurate climate data generation. Therefore it is required to consider the trend of observed precipitation and the characteristics of bias correction methods so that the generated precipitation can be used properly in water resource management plan establishment.