Twelve Annual Student Research Forum

Consumer telecommunication integrated circuits and wireless communication devices, such as cell phone systems and wireless local area network systems, require high-performance analog and radio frequency (RF) integrated circuits (ICs). As the technology advances, nowadays billions of transistors can be integrated into one single chip. Placement is essential to both digital and analog integrated circuits (IC) layout design. Consideration of various analog constraints, which makes analog placement design much complicated, can significantly improve analog IC performance. In this research proposal different general placement methods are firstly reviewed. Then we survey the techniques for handling analog placement. The proximity constraints, which define specified distances among components of interest, are increasingly demanded in the advanced technology. To address this challenge, we propose a type of specific proximity-aware placement constraints, called diagonal constraints, in the analog placement design. A new placement representation, namely diagonal-inclusive transitive closure graph (DI-TCG), is introduced to effectively and efficiently deal with the analog placement problem taking into account proximity-aware constraints. The fundamental properties of DI-TCG are analyzed and theoretically proved. We also provide insight into promising proximity-aware analog placement application with DI-TCG. Abstract: This research presents a computationally efficient stochastic climate model to simulate large scale climate variations for long term integration. In analogy with weather generators (a weather generator is a stochastic model that simulates daily weather based on the statistical characteristics of a local weather record), our model can be thought of as a Climate Generator (CG). Our CG, produces a synthetic time series of climatology data in a location, based on the statistical characteristic of inputs climatology data at that location. Inputs for the CG include the temperature field from a simplified Energy Balance climate model (EBM), surface elevation, ice mask, carbon dioxide, mithane and orbital forcing. The CG then outputs, mean monthly surface temperature and precipitation fields over North America for the period of Last Glacial Cycle (LGC,-118K to present day) by using Bayesian Artificial Neural Networks (BANN) as a nonlinear regression. These outputs will used to drive the Memorial University Glacial Systems Model (MUN GSM). We aspect, through development of this CG and its validation