Power-ground plane modeling using artificial neural network
| dc.contributor.author | Low, Chen Eng | |
| dc.date.accessioned | 2021-03-01T05:53:04Z | |
| dc.date.available | 2021-03-01T05:53:04Z | |
| dc.date.issued | 2019-06 | |
| dc.description.abstract | As technology advances, the increased complexity of electrical devices has gradually increased the number of variables that affect the output. This raises a problem whereby conventional simulation-based circuit design tools take time to run simulation, and circuit simulation must be done every time a design parameter is changed. Increasing design parameter in circuit design results in increasing time to run simulation, hence it takes longer time to solve circuit design problem. As a result, it has become prominent to explore new method to solve integrity analyses, without sacrificing accuracy and time. For past few decades, artificial neural network (ANN) neural network has emerged as a popular tool for solving electrical circuit problem. Neural network’s learning ability has become an alternative for conventional simulation method. In this work, power-ground plane is modelled based on their physical design parameter using both conventional method and neural network method. Sonnet Lite is used as conventional method to model the power-ground plane, whereas MATLAB is implemented to develop the neural network. Z-Parameter of the power-ground plane obtained from each method are compared, to justify the findings. The results indicate that the ANN achieved an accuracy of above 0.9 (with a reference of 1.0) and successfully modelled power-ground plane. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/11618 | |
| dc.language.iso | en | en_US |
| dc.title | Power-ground plane modeling using artificial neural network | en_US |
| dc.type | Other | en_US |
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