Ensemble Learning Based Electric Components Footprint Analysis

Abstract

Along with the rapid growth in the market of the Internet of Things and electrical devices, the design flow of Printed Circuit Boards (PCBs) requires a more effective design methodology. As to design a PCB board, it is necessary to build a footprint of components first, containing manufacturing information such as outline, height, and other constraints for placing components on a PCB board. Footprint design can vary between different manufacturers, depending on their production technology, which means an electronic component can have distinctive footprints. Therefore, analyzing PCB footprint libraries can help to sort out footprint design rules, which can then be used for designing new footprints of the same type of components. In this paper, we adopt StackNet based on the ensemble learning method, using footprint images and numerical information for classification. Furthermore, we implement hierarchical clustering on the classification result to analyze the footprint design rules. Experimental results show our method can achieve higher accuracy than previous works.