本文旨在探討台積電導入先進製程後，其相對生產力表現是否出現變化。近年來，人工智慧、高效能運算與先進製程需求快速成長，使全球半導體產業進入新一輪技術升級階段。台積電作為全球晶圓代工龍頭，其營收、資本支出與市值長期受到關注；然而，企業規模擴張或財務績效提升，並不必然代表生產效率改善。因此，本文以全要素生產力作為主要分析指標，檢驗台積電於先進製程導入後，相較其他半導體與光電產業公司是否呈現不同之生產力路徑。
本文研究期間為 2014 年至 2025 年，資料來源為台灣經濟新報資料庫之公司年度財務資料。實證方法上，本文首先採用 Levinsohn-Petrin 方法估計公司年度層級之全要素生產力，並以 revenue-based TFP 作為主要結果變數。其次，本文以台積電作為處理組，並以其他半導體與光電產業公司作為 donor pool，運用合成控制法建構台積電之反事實比較基準。本文以 2018 年作為主要處理時點，觀察台積電在 7 奈米先進製程進入量產階段後，其實際 LP TFP 是否相對合成控制組出現差異，並進一步透過不同潛在控制組樣本池與替代處理年份進行穩健性分析。
實證結果顯示，台積電在研究期間內具有高度特殊之企業規模與投入結構，其資本投入、營業收入與營運規模均明顯高於一般半導體與光電產業公司，顯示直接以產業平均作為對照組可能無法形成合理之反事實比較基準。合成控制法分析結果顯示，台積電於 2018 年後相對部分處理前配適較佳之 潛在控制組樣本池 呈現較高之 LP TFP 路徑，表示先進製程導入後，台積電相對特定比較基準之生產力表現有所提升。然而，並非所有半導體子產業潛在控制組樣本池l 皆呈現一致正向結果，部分子產業之估計結果為負向或不穩定，顯示先進製程之生產力效果並非全面且均質地擴散至所有半導體相關產業。

This study examines whether Taiwan Semiconductor Manufacturing Company (TSMC) experienced changes in relative productivity after adopting advanced process technologies. As artificial intelligence, high-performance computing, and demand for advanced chips continue to grow, advanced semiconductor processes have become increasingly important. Although TSMC has attracted attention for its revenue growth and capital expenditure, firm expansion does not necessarily imply productivity improvement. Therefore, this study uses total factor productivity (TFP) to examine whether TSMC exhibits a distinct productivity path relative to other semiconductor and optoelectronic firms.
Using annual firm-level financial data from the Taiwan Economic Journal (TEJ) database from 2014 to 2025, this study estimates firm-level TFP using the Levinsohn-Petrin (LP) method and applies the synthetic control method (SCM) to construct a counterfactual benchmark for TSMC. TSMC is treated as the treated unit, while other semiconductor and optoelectronic firms serve as the donor pool. The main treatment year is set to 2018, corresponding to TSMC’s 7-nanometer process mass production.
The empirical results show that TSMC differs substantially from other firms in scale and input structure, suggesting that industry averages may not provide an appropriate counterfactual benchmark. After 2018, TSMC exhibits a higher LP TFP path relative to selected donor pools with good pre-treatment fit, though the results vary across subindustries.
Overall, this study suggests that TSMC’s advanced process adoption is associated with a higher relative revenue-based TFP path under specific comparison benchmarks. Since TFP is estimated from financial data, the results should be interpreted as relative output-value productivity rather than pure physical productivity.

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