Automated Placement Of A Transistor Pair For Analogue

The performances of analogue circuits are affected by surrounding parameters such as levels of noise, thermal gradients of a circuit, and parasitic effects from both resistive and capacitive part. As there are no effective approaches to handle these analogue constraints as mentioned above, the focuses to develop IC design tools are bended towards digital circuits. The purpose of this research is to introduce a complete methodology for transistor pair placement for analogue layout using a concept of cells and arrays based on migration and reuse. The entire process consists of Standard Cell Generation to produce standard cell for analogue circuits, Matching Generator with array alignment to generate transistor matching of common-centroid arrangement, and Auto Routing for global routing. The methodology is translated into automation by a graphical user interface to render a fully functional layout designs in a few steps and fraction of time. This research describes such a system in obtaining a layout that can be configured like a set of building blocks that meets all design specifications. In comparison to all the different approaches that have been discussed and analysed prior to this research, a new design flow for analogue layout combined with automation is constructed by considering transistor matching as a constraint

Doctor of Philosophy

dissertationGraphics processing units (GPUs) are highly parallel processors that are now commonly used in the acceleration of a wide range of computationally intensive tasks. GPU programs often suffer from data races and deadlocks, necessitating systematic testing. Conventional GPU debuggers are ineffective at finding and root-causing races since they detect errors with respect to the specific platform and inputs as well as thread schedules. The recent formal and semiformal analysis based tools have improved the situation much, but they still have some problems. Our research goal is to aply scalable formal analysis to refrain from platform constraints and exploit all relevant inputs and thread schedules for GPU programs. To achieve this objective, we create a novel symbolic analysis, test and test case generator tailored for C++ GPU programs, the entire framework consisting of three stages: GKLEE, GKLEEp, and SESA. Moreover, my thesis not only presents that our framework is capable of uncovering many concurrency errors effectively in real-world CUDA programs such as latest CUDA SDK kernels, Parboil and LoneStarGPU benchmarks, but also demonstrates a high degree of test automation is achievable in the space of GPU programs through SMT-based symbolic execution, picking representative executions through thread abstraction, and combined static and dynamic analysis

Cartographic Generalization in Digital Environment

Throughout the world numerous efforts to automate generalization are in progress. The results are yet to be satisfactory. Ample reasoning can be given to justify the lack of success, the most important being that generalization is an ambiguous process, highly subjective which lacks definitive rules, guidelines or systematization. This paper deals with the problem of generalization of vector data bases through the analysis of recent developments and research in the field. These developments tend to establish a promising framework which, with subsequent refinements and the utilization of state-of-the-art computer technology, may lead to successful results. What is needed is what lacks: Definitive rules in structuring the digital image of the world and development of expert systems which will intelligently manipulate this image

Analogue filter networks: developments in theory, design and analyses

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