隨著半導體技術的快速發展，IC載板成為電子產品微型化和高效能化的重要基礎，廣泛應用於高效能運算、車載電子、消費性電子等領域；然而，IC載板的生產過程涉及多種高精度製程技術，品質控管極具挑戰性。其中，盲孔品質問題是最常見且最棘手的客訴類型，因其缺陷難以在廠內檢測發現，往往是在客戶端的熱製程或終端測試才顯現異常，造成高額品質成本與客戶信任危機。為解決此類問題，本研究透過8D問題解決方法與QC七大手法，對IC載板產業中的U公司進行實際案例分析，探討如何有效降低盲孔底部裂痕的發生率，提升品質管理能力，並建立系統化的客訴改善流程。
本研究以U公司的客訴案例為基礎，詳細記錄8D手法如何應用於客戶抱怨處理；首先，研究團隊透過D1至D3步驟（建立團隊、問題描述、暫時性對策），迅速組織跨部門專家團隊，分析異常批次的IC載板，並利用5W2H問題分析法探索潛在風險；同時，透過可靠度測試、聚焦離子束顯微鏡分析及能量色散X光光譜等手段，驗證問題發生的根本原因，發現化學銅沉積厚度不足、盲孔底部氧化等製程缺陷是裂痕形成的主因；接著，根據D4至D6步驟（找出根本原因、擬定對策、執行與驗證），制定化銅製程參數優化、盲孔雷射鑽孔流程改善等措施，並透過管制圖、魚骨圖等QC工具進行數據監控，確保改善方案有效執行；最後，在D7至D8步驟（預防再發、團隊表彰）中，研究團隊透過標準作業程序更新、製程風險評估等機制，確保改進措施能夠長期維持，並進一步降低未來客訴發生的可能性。
研究結果顯示，透過8D手法與QC七大手法的整合應用，U公司成功解決盲孔底部裂痕，並有效縮短客訴處理時間，提高客戶滿意度；此外，本研究亦提供一個標準化問題解決流程，可作為IC載板產業應對類似品質問題的參考模型；未來，IC載板製造商可進一步結合AI智能檢測、機器學習分析製程數據等技術，提升異常預測與防範能力，從根本上減少品質缺陷。本研究證明，透過系統化的問題解決架構與品質管理工具的應用，企業能夠更有效地應對客訴問題，提升整體競爭力，並在高度競爭的半導體產業中保持領先地位。

With the rapid advancement of semiconductor technology, IC carrier have become a crucial foundation for the miniaturization and high-performance capabilities of electronic products. They are widely used in high-performance computing (HPC), automotive electronics, and consumer electronics. However, the production of IC carrier involves multiple high-precision manufacturing processes, making quality control highly challenging. Among the various quality issues, via quality defects are the most common and difficult customer complaints to handle. These defects are often undetectable during in-house inspections and only become apparent during thermal processes or final testing on the client side, leading to high quality costs and customer trust crises. To address these issues, this study applies the 8D problem-solving methodology and the Seven Basic QC Tools to conduct a case study on Company U in the IC carrier industry. The research aims to explore effective strategies to reduce the occurrence of via bottom cracks, enhance quality management capabilities, and establish a systematic approach to customer complaint resolution.
This study is based on a customer complaint case from Company U and documents in detail how the 8D methodology was applied to handle customer issues. First, through the D1 toD3 steps(team formation, problem description, and interim containment actions), a cross-functional expert team was quickly assembled to analyze the defective batches of IC substrates. The team employed the 5W2H problem analysis method to identify potential risks. Additionally, reliability testing, focused ion beam (FIB) analysis, and energy-dispersive X-ray spectroscopy (EDS) were conducted to verify the root causes of the defects. The findings revealed that insufficient chemical copper deposition and oxidation at the via bottom were the primary reasons for crack formation. Subsequently, following the D4 to D6 steps (identifying root causes, developing corrective actions, and implementing & validating solutions), the study proposed optimization of chemical copper process parameters and improvements in the laser via drilling process. These corrective actions were monitored using control charts, Ishikawa diagrams (fishbone diagrams), and Pareto charts, ensuring the effectiveness of the implemented solutions. Finally, in the D7 to D8 steps (prevent recurrence and recognize team efforts), the team ensured the sustainability of these improvements by updating standard operating procedures (SOPs), conducting failure mode and effects analysis (FMEA), and providing employee training, further minimizing the likelihood of future customer complaints.
The results demonstrate that integrating the 8D methodology with the Seven QC Tools enabled Company U to successfully resolve via bottom crack issues, significantly reduce
customer complaint handling time, and improve customer satisfaction. Additionally, this study provides a standardized problem-solving framework, which can serve as a reference model for the IC carrier industry in addressing similar quality challenges. In the future, IC carrier manufacturers can further integrate AI-based intelligent inspection and machine learning-driven process data analysis to enhance defect prediction and prevention capabilities, ultimately reducing quality issues at the source. This research confirms that by applying a systematic problem-solving approach and quality management tools, companies can respond more effectively to customer complaints, enhance overall competitiveness, and maintain a leading position in the highly competitive semiconductor industry.

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