隨著網際網路和電子設備市場的快速增長，印刷電路板的設計流程需要更有效率的設計方法。對於印刷電路板的設計，首先需要構建組件的封裝，其中包含製造信息，例如輪廓、高度和其他將組件放置在印刷電路板上的限制條件。

不同製造商之間的封裝設計規則可能會有所不同，這完全取決於他們的生產技術，所以每個電子元件的足跡圖樣在不同製造商之間也會採用不同樣式。我們可以藉由分析該製造商的印刷電路板封裝資料庫來整理他們的封裝設計規則，最後用於設計相同類型元件的新封裝。
在本文中，我們採用基於集成學習方法的StackNet，使用電子元件足跡圖樣和數值特徵進行分類。

實驗結果表明，我們的方法可以達到比以前的工作更高的正確率。此外，我們對分類結果使用階層式分群法來分析足跡圖樣的設計規則。

Along with a rapid growth in the market of the Internet of Things and electronic devices, the design flow of Printed Circuit Boards (PCBs) requires a more effective design methodology.
As to designing a PCB board, it is necessary to build a component footprint first, which contains 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 electric 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 thesis, 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 achieved higher accuracy than previous works.

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