本研究指在運用決策樹模型分析EDA（電子設計自動化）軟體數據，以識別導致IC Package Design不符合規格（Out Spec）的原因，並藉此加速設計週期。在現代IC設計中，設計週期的縮短和設計品質的提高對於市場競爭力至關重要。針對IC Package Design為了實現這一目標，我們首先收集了來自多個設計項目的EDA數據。 在數據預處理階段，我們對這些數據進行了特徵選擇等分析，以確保分析的準確性和效率。隨後，我們使用決策樹模型對數據進行建模，通過訓練和測試模型來評估其預測準確性和穩定性。決策樹模型的優勢在於其能夠清晰地展示影響設計結果的關鍵因素，並以樹狀結構直觀地呈現決策過程。 通過對模型結果的分析，我們識別出了若干關鍵參數，這些參數對IC Package Design是否超出規格具有顯著影響。例如，設計尺寸和材料特性的微小變動可能會導致設計結果的大幅偏差。我們進一步分析了這些關鍵參數之間的相互作用，並提出了針對性的優化建議。 研究結果表明，通過調整這些關鍵參數，可以顯著提高設計結果的合規性，從而減少設計迭代次數，縮短設計週期。此外，這些優化建議還有助於降低設計過程中的風險和成本，提升整體設計效率。 本研究通過決策樹模型對EDA數據的深入分析，成功識別了IC Package Design超出規格的主要原因，並提出了有效的優化方案。這不僅有助於加速IC Package Design設計週期，還為今後的設計工作提供了寶貴的經驗和參考。 ;This study employs decision tree models to analyze data from Electronic Design Automation (EDA) software to identify the root causes of out-of-spec (Out Spec) issues in IC Package Design, thereby expediting the design cycle. In contemporary IC design, the reduction of design cycles and the enhancement of design quality are imperative for maintaining market competitiveness. To achieve this objective in IC Package Design, we initially collected EDA data from various design projects. In the data preprocessing phase, we conducted feature selection and other analyses to ensure the accuracy and efficiency of the analysis. Subsequently, we utilized decision tree models to construct a predictive model, evaluating its accuracy and stability through training and testing. The advantage of decision tree models lies in their ability to clearly demonstrate the key factors influencing design outcomes and present the decision-making process in an intuitive tree structure. Through the analysis of the model results, we identified several key parameters that significantly impact whether the IC Package Design meets specifications. For instance, minor variations in design dimensions and material properties can lead to substantial deviations in design outcomes. We further examined the interactions between these key parameters and proposed targeted optimization recommendations. The findings indicate that adjusting these key parameters can significantly enhance design compliance, thereby reducing the number of design iterations and shortening the design cycle. Additionally, these optimization recommendations help to mitigate risks and costs during the design process, thereby improving overall design efficiency. By conducting an in-depth analysis of EDA data using decision tree models, this study successfully identified the primary causes of Out Spec issues in IC Package Design and proposed effective optimization strategies. This not only aids in accelerating the design cycle of IC Package Design but also provides valuable insights and references for future design endeavors.
