Modular machine learning based circuit design

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dc.contributor.authorEkarna, Jacob
dc.contributor.authorLind, Erik
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mikroteknologi och nanovetenskap (MC2)sv
dc.contributor.departmentChalmers University of Technology / Department of Microtechnology and Nanoscience (MC2)en
dc.contributor.examinerFager, Christian
dc.contributor.supervisorSjödin, Martin
dc.date.accessioned2024-08-21T08:45:49Z
dc.date.available2024-08-21T08:45:49Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractIn traditional circuit design a pre-selected circuit topology is optimized through time consuming parameter sweeps to satisfy a design criteria. A newly introduced design concept instead utilizes machine learning models to predict the transfer function of a given circuit structure, together with a genetic algorithm to generate a circuit based on wanted scattering parameters. The concept of machine learning in circuit design, however, has its drawbacks. One notable drawback is that the circuits generated from the model are all of the same size the model was trained on, leading to scalability issues. To overcome this problem this thesis evaluated whether or not it is possible to use a machine learning model trained on a dataset of smaller 9 × 9 circuits to create a larger modular circuit, consisting of four modules. The generated modular circuits were assessed by comparing the predicted scattering parameters from the optimization to the pre-selected target parameters. Additionally, simulations were performed on the generated circuits and the results were compared with the predicted parameters. The thesis also investigated if the implementation of a via fence could help isolate the modules from eachother to reduce electromagnetic interference and improve performance. The differences in time efficiency between the two cases were also compared. The results show that the modular concept works to a high degree. Based on simulation results, the root mean square error for the scattering parameters for the non-via fence model was 0.05934 and for the via fence model it was 0.04677. Adding a via fence improves the model predictions slightly and further improves the simulated circuits significantly. The results for the circuit designs with a via fence, over 100 generated circuits designs, were 13 % more accurate than the circuit designs without a via fence. However, this came at the cost of increased simulation time, as circuits using a via fence took a considerably longer time to simulate.
dc.identifier.coursecodeMCCX04
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308444
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectCircuit design, Modular, Scattering parameters, Machine Learning, Genetic Algorithm
dc.titleModular machine learning based circuit design
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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