半導體產業是科技進步的核心，2018-2022年之間前所未有的投資增長突顯該產業對高性能運算和先進製造技術需求的回應，其中半導體設備製造商在半導體的領域中扮演關鍵角色，因此半導體設備公司如何有效運用有限資源以面對未來的挑戰，成為重要的議題。
本研究旨在提供全球半導體設備公司經營績效的實證分析，研究2018年到2022年15家關鍵全球半導體設備製造公司，並採用RDM DDF model結合動態網絡DEA模型之Two-stage Dynamic RDM DDF model，本研究克服處理如每股盈餘等負值數據點的限制，並且引入無形資產作為比專利數更全面的輸出變量，考慮到更加完整之技術輸出與品牌價值。以下歸納本文之研究結果：
1. 半導體設備公司在2018年至20222年整體平均效率值為0.78，在研究期間內每年整體平均值沒有大幅變動，從個別公司來看，Advanced Energy、ASML、Lam Research之5年平均效率值為1.00，為效率前緣公司。
2. 整體而言，半導體設備公司在生產階段之效率優於市場階段。研究對象中的多數公司在生產階段表現出較高的效率，平均效率值為0.92，並且有11家公司連續5年在生產階段保持最佳效率值1.00，大多數受評公司在生產上具有高效率，然而在市場階段，整體平均效率值為0.65，指出有名顯的提升空間。
3. 生產階段方面，各公司在員工人數投入效率上相對有改善空間，而在市場階段每股盈餘和平均股價平均效率值為0.84和0.81，半導體設備產業對於兩個產出表現均有明顯提升空間。

The semiconductor industry is pivotal to technological advancement, and semiconductor equipment manufacturers play a crucial role within this sphere; hence, the efficiency with which semiconductor companies utilize their resources to face future challenges has become a significant issue. This study aims to bridge the research gap by providing an empirical analysis of the operational efficiency of global semiconductor equipment companies. It focuses on 15 key semiconductor equipment manufacturers worldwide from 2018 to 2022, employing the Two-stage Dynamic RDM DDF model. The findings of this study are summarized as follows:
(1) Semiconductor equipment companies maintained an overall average efficiency score of 0.78 from 2018 to 2022, with no significant annual fluctuations, and 3 companies among them achieved an average efficiency score of 1.00 over five years.
(2) Overall, semiconductor equipment companies exhibited higher efficiency in production than in the market stage. Most companies demonstrated high efficiency in the production stage, with an average efficiency value of 0.92. However, the overall average efficiency value in the market stage was 0.65.
(3) Regarding the production stage, the efficiency of workforce utilization across companies can be improved. In the market stage, the average efficiency values for earnings per share and average stock price were low, which were 0.84 and 0.81.

李揚、鐘棠祺（2015）。半導體產業之績效評估－跨國分析與比較。東吳經濟商學學報，(89)，61-88。
李東杰、林宏樹（2015）。應用共同邊界資料包絡分析法探討上市櫃半導體企業之績效。管理研究學報，15(1)，1-33。
呂正欽、高淩菁、傅新彬（2016）。應用 ICA 與DEA 方法評估半導體產業封測領域公司經營績效－以台灣中型半導體封測廠為例。中山管理評論，24(3)，503-530。
洪志洋、衛俊宏、姜榮新、許楨屏（2013）。探索企業智慧資本的量度與經營績效－以全球十大半導體設備公司為例。評價學報，(6)，27-51。
蔡榮發、張淑娟、張原嘉（2016）。台灣上市半導體公司經營績效評估與分析。創新與管理，12(1)，111-136。
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and Undesirable Outputs: A Directional Distance Function Approach. Journal of Environmental Management, 51(3), 229-240.
Chen, Y., & Chen, B. (2009). Using data envelopment analysis (DEA) to evaluate the operational performance of the wafer fabrication industry in Taiwan. Journal of Manufacturing Technology Management, 20, 475-488.
Chen, Y., & Chen, B. (2011). Applying DEA, MPI, and grey model to explore the operation performance of the Taiwanese wafer fabrication industry. Technological Forecasting and Social Change, 78, 536-546.
Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.
Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50(1), 65-70.
Färe, R., & Grosskopf, S. (2004). Efficiency and productivity: New directions. In: Boston, MA: Kluwer Academic Publishers.
Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network dea. In Modeling data irregularities and structural complexities in data envelopment analysis (pp. 209-240): Springer.
Färe, R., & Grosskopf, S. (2010). Directional distance functions and slacks-based measures of efficiency. European Journal of Operational Research, 200(1), 320-322.
Fang, T. (2020). Who Is the Keyman? Integrating Two-Stage DEA and Social Network Analysis to Evaluate Operational and Environmental Efficiency in the Semiconductor Industry. Mathematical Problems in Engineering ,2020, 1-11.
Huang, M. Y., & Huang, S. Y. (2010). Productivity evaluation of Taiwanese semiconductor41 companies using a three-stage Malmquist DEA approach. 應用經濟論叢, (87_S), 31- 57.
Hung, S., He, D., Lu, W., & Cheng, M. (2013). Business groups' dynamic performance of Taiwan semiconductor industry: the carry-over perspective. 2013 Proceedings of PICMET '13: Technology Management in the IT-Driven Services (PICMET),2013, 2770-2775.
He, D., Liu, T., & Lin, Y. (2024). Efficiency evaluation of intellectual capital for value creation:a case study of the semiconductor industry in Taiwan. Journal of Intellectual Capital, 25 (4), 744-774.
Koopmans, T. i. C. (1957). Three Essays on the State of Economic Science. New York: McGraw-Hil1 Book Company. In: Inc.
Klopp, G. A. (1985). The analysis of the efficiency of productive systems with multiple inputs and outputs. University of Illinois at Chicago.
Liu, F.F., & Wang, P. (2008). DEA Malmquist productivity measure: Taiwanese semiconductor companies. International Journal of Production Economics, 112, 367- 379.
Lu, W., & Hung, S. (2010). Assessing the performance of a vertically disintegrated chain by the DEA approach–a case study of Taiwanese semiconductor firms. International Journal of Production Research, 48, 1155 - 1170.
Lin, R., & Chen, Z. (2015). Super-efficiency measurement under variable return to scale: an approach based on a new directional distance function. Journal of the Operational Research Society, 66(9), 1506-1510.
Lin, F., Lin, S., & Lu, W. (2018). Sustainability Assessment of Taiwan’s Semiconductor Industry: A New Hybrid Model Using Combined Analytic Hierarchy Process and Two- Stage Additive Network Data Envelopment Analysis. Sustainability, 10(11), 1-16.
Li, H., He, H.Y., Shan, J., & Cai, J. (2019). Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis. Socio- Economic Planning Sciences, 66, 136–148.
Lin, C., & Ding, G. (2021). On the Efficiency of China’s Semiconductor Industry Based on the Meta-frontier DEA. Proceedings of the 2021 International Conference on Modern Management and Education Research (MMER 2021).
Portela, M. C. A. S., Thanassoulis, E., & Simpson, G. (2004). Negative data in DEA: a directional distance approach applied to bank branches. Journal of the Operational Research Society, 55(10), 1111-1121.
Pan, Y., Song, Y., Zhang, C., Zhu, Q., & Wu, J. (2024). Semiconductor industry's transformation path under circular economy: Evidence from micro-level efficiency evaluation. Comput. Ind. Eng., 189, 109934.
Sueyoshi, T., & Sekitani, K. (2005). Returns to scale in dynamic DEA. European Journal of Operational Research, 161(2), 536-544.
Seiford, L. M., & Zhu, J. (2005). A response to comments on modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 161(2), 579-581.
Sueyoshi, T., & Ryu, Y. (2020). Performance Assessment of the Semiconductor Industry: Measured by DEA Environmental Assessment. Energies, 13(22), 5998.
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis.42 European Journal of Operational Research, 130(3), 498-509.
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243-252.
Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3), 145-156.
Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124-131.
Wu, H., Chen, I., Chen, J., & Chien, C. (2019). The R&D efficiency of the Taiwanese semiconductor industry. Measurement, 137, 203-213.
Yu, M.-M. (2004). Measuring physical efficiency of domestic airports in Taiwan with undesirable outputs and environmental factors. Journal of Air Transport Management, 10(5), 295-303.