半導體產業與我們現在的生活密不可分，而半導體產業屬於高技術產業，必須藉由不斷的研發與創新才能保持其不被市場所淘汰，在DRAM市場上的競爭尤為激烈，目前以美光科技、SK海力士與三星獨大，三家廠商的市占率高達95%，為一寡占市場。本研究藉由半導體1980年到2021年的專利文獻建構專利引用網絡，並進一步找尋核心專利分布的廠商，以此探討DRAM廠商在市場競爭時，所掌握的核心技術與市場表現之關聯。研究結果顯示，在DRAM產業中，廠商擁有越多的核心專利，並且專業化集中於少數技術領域，能達到更好的財務表現。因此，創新是DRAM公司在成長過程中一個很重要的因素，擁有更多核心專利與專業化的技術，在未來的產業發展上可為公司帶來更多獲利。

Understanding key successful factors (KSF) in a competition is crucial to a firm's sustainable growth in the relative business environment. For example, the Dynamic Random Access Memory (DRAM) industry has long been believed as the most competitive field in semiconductor manufacturing and the business’s exit is rampant. In this vein, this research intends to identify the KSFs for DRAM firms' survival. Via the computation of DRAM patents' citation networks, the main components of DRAM patents are back out and then utilized to DRAM firms' patent concentration as well as intensity index. The results show that those DRAM firms with their patent development concentrated in specialized areas will benefit the most from their long-term survival and sustainable growth in the operational financial performance in the industry.

中文文獻
王苡潼，(2018)。從三星的策略看DRAM產業，TEJ信用風險評估專刊。
李文傑，(2022)。資源錯置與產業動態：以台灣的動態隨機存取記憶體產業為例。
金捷幡，(2019)。【歷史】內存的故事 (紀念DRAM量產50年)，擷取日期：2022 年 5 月 30 日，網址：https://kknews.cc/tech/o3kmgxq.html
陳玠瑋，(2011)。2011 年全球 DRAM 產業發展趨勢，工研院產業經濟與趨勢研究中心。
彭茂榮，(2014)。2014 年全球 DRAM 產品發展趨勢，工研院產業經濟與趨勢研究中心。
彭茂榮，(2017)。2017 年全球 DRAM 產品發展趨勢，工研院產業經濟與趨勢研究中心。
彭茂榮，(2017)。全球記憶體產品市場發展趨勢與主要廠商，工研院產業經濟與趨勢研究中心。
彭茂榮，(2021)。各國半導體產業發展政策概況，工研院產業經濟與趨勢研究中心。
彭國柱，(2008)。DRAM 混戰：韓廠進逼、台廠且戰且走、 日廠迂迴鞏固戰略高地，工研院產業經濟與趨勢研究中心。
劉美君，(2019)。突破與創新-看全球記憶體技術與市場發展變動趨勢，工研院產業經濟與趨勢研究中心。
劉美君，(2020)。蛻變與新生-2019 年全球記憶體產業回顧 與 2020 年展望，工研院產業經濟與趨勢研究中心。
蘇孟宗，(2013)。2013-2015科技與產業趨勢，工研院產業經濟與趨勢研究中心。
英文文獻
Bahk, B.-H. and Gort, M. (1993). Decomposing Learning by Doing in New Plants. Journal of Political Economy. 101(4), 561–583.
Benkard, C. L. (2000). Learning and Forgetting: The Dynamics of Aircraft Production. American Economic Review. 90(4), 1034–1054.
Camisón, C. and A. Villar-López (2014), Organizational innovation as an enabler of technological innovation capabilities and firm performance, Journal of Business Research, 67(1), 2891-2902.
Connolly, R.A. & Hirschey, M. (1988). Market Value and Patents: A Bayesian Approach. Economics Letters, Vol. 27, pp. 83.
Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through Association of Ideas. Science, 122(3159), 108-110.
Hashi, I. and N. Stojčić (2013), The impact of innovation activities on firm performance using a multi-stage model: Evidence from the Community Innovation Survey 4, Research Policy, 42(2), 353-366.
Hirshleifer, D., P. H., Hsu, and D. Li (2013). Innovative efficiency and stock returns. Journal of Financial Economics, 107(3), 632–54.
Hummon, N.P. & Doreain, P. (1989). Connectivity in a Citation Network: The Development of DNA Theory. Social Networks, 11, 39-63.
Jarmin, R. S. (1994). Learning by Doing and Competition in the Early Rayon Industry. RAND Journal of Economics, 25(3), 441–454.
Kim, D & Kim, N & Kim, W. (2018). The effect of patent protection on firms’ market value: the case of the renewable energy sector. Renewable and Sustainable Energy Reviews, 2018; 82: 4309-19.

Lanjouw, J. O. & Schankerman, M. (2004). Patent quality and research productivity: Measuring innovation with multiple indicators. The Economic Journal, 114(495), 441-465.
Lin, B. W., C. J., Chen, H. L., Wu (2006). Patent portfolio diversity, technology strategy, and firm value. IEEE Transactions on Engineering Management, 53(1), 17-26.
Ma, Daw and John Mark (2003). The DRAM Market Structure: The Rise and Fall in Concentration. The 8th Annual Cambridge International Manufacturing Symposium, Cambridge, UK.
Markman, G. D., M. I., Espina, and P. H., Phan (2004). “Patents as Surrogates for Inimitable and Non-Substitutable Resources”, Journal of Management, 30(4), 529-544.
Murillo, L.E. (1993). “The International Dynamic Random Access Memory Industry From 1970 to 1993 Examined Under the Dynamic Capabilities Prism: Implications for Technology Policy [Ph.D. Thesis],” University of California, Berkeley.
Newman, Mark. (2004). Fast algorithm for detecting community structure in networks. Physical review E, 69(6), 066133.
Singh A. & Triulzi G. & Magee C. (2021). Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description. Research Policy, 50.
Thompson, P. (2001). How Much Did the Liberty Shipbuilders Learn?: New Evidence for an Old Case Study. Journal of Political Economy, 109(1), 103–137.
Thornton, R. A. & Thompson, P. (2001). Learning from Experience and Learning from Others: An Exploration of Learning and Spillovers in Wartime Shipbuilding. American Economic Review, 91(5), 1350–1369
Trajtenberg, M., R. Henderson & A. Jaffe, (1997). University Versus Corporate Patents: A Window on The Basicness of Invention. Economics of Innovation and New Technology, vol. 5, no. 1, pp. 19-50.
Triulzi, G. (2015). Looking for the right path: technology dynamics, inventive strategies and catching-up in the semiconductor industry. Maastricht: Datawyse /Universitaire Pers Maastricht.
Triulzi, G. & Alstottc, J. & Magee C. (2020). Estimating technology performance improvement rates by mining patent data. Technological Forecasting & Social Change,158.
Valentini, G. (2012). Measuring the effect of M&A on patenting quantity and quality. Strategic Management Journal (33), 336-346.
Verspagen, B. (2007). Mapping Technological Trajectories as Patent Citation Networks. A Study on the History of Fuel Cell Research. Advances in Complex Systems 10, 93-115.