Ciencias Exactas y Ciencias de la Salud

This thesis documents my personal research as candidate for the academic degree of Master of Science in Intelligent Systems. The purpose of this work is to optimize the electronic components layout in a printed circuit board based on metrics related to its thermal dissipation. The continuous evolution of smaller, more complex and compact integrated circuits pushes the design and fabrication techniques of printed circuit board, also known as PCB, to new limits. Moore’s Law states: “The number of transistors incorporated in a chip will approximately double every 24 months.” Nowadays, it is possible to see several examples of this in the new microprocessors used in computers, smart phones and tablets that have more than 600 million transistors (e.g. Apple A10, Intel Core i9, Qualcomm Snapdragon). A direct result of this is reflected in a significant and dramatic complexity of packages and interconnection traces contained in a single integrated circuit. Integrated circuits are increasingly dense and perform a large number of operations, requiring more current consumption and generating a rise in the circuit temperature that needs to be dissipated through the environment. The optimal placement of electronic components over a PCB can ameliorate the problem, but requires meeting multiple design objectives mostly due to the dierent power dissipation of the components, their operating temperatures, kind of materials, terminals and dimensions. It is important to notice that to approach the complexity of this type of problems, heuristics methods such as the proposed in this work, are required. Although many global companies focus in the design of electronics devices and invest in computer aided design software, specifically in Electronic Design Automation platforms known as EDA, to expedite prototype development, just a few of them have the necessary computational tools that can automatically meet all the components placement constraints. Particle Swarm Optimization (PSO) is used to address the problem through a weighted sum method for multi-objective optimization (MOO). Regardless of the deficiencies with respect to depicting the Pareto optimal set, the weighted sum method continues to be used extensively not only to provide multiple solution points by varying the weights consistently, but also to provide a single solution point that reflects preferences presumably incorporated in the selection of a single set of weights. Experiments performed were based on three main categories. Broadly speaking, the first one consisted on identifying the most important properties of PSO algorithm, its response and behavior under a dierent set of scenarios. The second kind of experiments aimed to observe and analyze which variables had more impact, and how these dominate over the rest. Lastly, the third kind cared to analyze the response of the algorithm under more complex instances of a problem. Results produced by the PSO algorithm were compared against a finite element analysis software, and finally, a general discussion was elaborated, where validity was given to the hypothesis proposed in this paper when analyzing the performance shown

Ciencias Exactas y Ciencias de la Salud

Browse

Filters

Settings

Sort By

Results per page

Search Results