探討降低驅動IC成品最終測試功能性良率損失方法之研究

Abstract

本研究旨在探討如何降低驅動IC最終測試（Final Test, FT）階段的功能性良率損失，以提升半導體封裝測試廠的競爭力。隨著台灣半導體產業在全球供應鏈中的關鍵地位日益凸顯，測試環節對於確保產品品質及提升良率管理的重要性不容忽視。本研究聚焦於個案公司（封裝測試A公司）的實際生產數據，並運用品質改善手法（QC STORY）作為核心分析框架，系統性剖析影響良率的關鍵原因。 研究方法方面，本研究結合限制理論（Theory of Constraints, TOC），識別影響良率的主要瓶頸，並運用創新發明理論（俄文Teoriya Resheniya Izobretatelskikh Zadatch, TRIZ）解析技術矛盾，尋找最佳解決方案。此外，透過實驗設計（Design of Experiments, DOE），深入分析影響參數並驗證造成良率損失的根本原因。同時，本研究導入戴明循環（PDCA）機制，以確保改善措施的有效執行與持續優化。 研究結果顯示，透過DOE實驗所確立的最佳參數配置，並搭配系統性優化與調整措施，能夠有效降低測試過程中影響良率的多項關鍵因素，進一步提升產品的穩定性與測試準確度。此改善方案不僅有助於減少因製程變異所導致的品質問題，亦能增強測試流程的可靠性，使產品在最終測試階段的表現更趨穩定。綜合而言，本研究所採用的優化策略對於提升整體產品良率具有顯著成效，並為企業營運帶來實質效益。 本研究不僅為個案公司建立了一套可行的良率提升機制，亦為其他封裝測試業者提供具參考價值的改善模式，以優化生產流程、降低成本並提升競爭力。未來研究可進一步探討人工智慧技術在良率管理中的應用，例如透過機器學習與大數據分析，實現異常模式的自動識別、測試流程優化與良率變動預測，以提升問題解決的效率與準確性，進一步強化半導體封測產業的智能化管理能力。

This study aims to explore how to reduce the functional yield loss in the final test (FT) stage of driver ICs in order to enhance the competitiveness of semiconductor packaging and testing plants. As Taiwan's semiconductor industry becomes increasingly important in the global supply chain, the importance of testing in ensuring product quality and improving yield management cannot be ignored. This study focuses on the actual production data of a case company (Packaging and Testing Company A), and uses the QC STORY quality improvement method as the core analysis framework to systematically analyze the key reasons that affect yield. In terms of research methods, this study combines the Theory of Constraints (TOC) to identify the main bottlenecks affecting yield, and uses TRIZ innovative invention theory to analyze technical contradictions and find the best solution. In addition, through Design of Experiments (DOE), we can deeply analyze the influencing parameters and verify the root causes of yield loss. At the same time, this study introduced the PDCA cycle mechanism to ensure the effective implementation and continuous optimization of improvement measures. The research results show that the optimal parameter configuration established through DOE experiments, combined with systematic optimization and adjustment measures, can effectively reduce multiple key factors affecting the yield during the test process, further improving product stability and test accuracy. This improvement plan not only helps reduce quality issues caused by process variation, but also enhances the reliability of the testing process, making the product's performance more stable in the final testing stage. In summary, the optimization strategy adopted in this study has significant effects on improving the overall product yield and brings substantial benefits to corporate operations. This study not only established a feasible yield improvement mechanism for the case company, but also provided a reference improvement model for other packaging and testing companies to optimize production processes, reduce costs and enhance competitiveness. Future research can further explore the application of AI technology in yield management. For example, through machine learning and big data analysis, it can realize automatic identification of abnormal patterns, test process optimization and yield change prediction, so as to improve the efficiency and accuracy of problem solving and further enhance the intelligent management capabilities of the semiconductor packaging and testing industry.