Genetic optimization applied to via and route strategy
Author(s)Zumbo, Zachary J.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Jacob White and Taylor Hogan.
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To meet increased demand and higher PCB design expectations, research engineers have been tasked to develop models to automate PCB placement and routing procedures using machine learning and artificial intelligence techniques. Since placement and routing are still tedious manual processes which limit the design search space, these techniques allow engineers to quickly investigate better solutions. The Move37 team within Cadence Design Systems found via placement to be a crucial problem to be solved and integrated into their OrbitIO platform. We evaluated multiple non-gradient-based optimization strategies and compiled data of their performance. From these tests, a genetic algorithm-based strategy was sought due to its fast convergence and the ability to substitute cost functions. In this study, we converted the via placement problem to a simpler layer assignment problem by enforcing the location of vias and pins to be the same. We then determined an optimal layer assignment given a set of flylines, i.e. logical connections, using a genetic optimization library called DEAP. We coined this genetic optimization approach to via strategy GO VIA.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020Cataloged from student-submitted PDF of thesis.Includes bibliographical references (page 28).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.