隨著台灣半導體、液晶顯示器等高科技產業製程精密度不斷提升，對於生產環境的要求已達到近乎嚴苛的標準。傳統的大型無塵室因難以精準管控局部氣流，已逐漸被能提供超潔淨環境的微環境所取代。不過，在實際營運過程中，微環境內部仍會受到複雜流場結構與生產設備和各種機器佈局的影響，導致微粒子污染風險，不僅會造成產品瑕疵，也會使環境處於較為震盪、不穩定的情況。本研究針對國內某高科技廠之無塵室內部環境，旨在透過高精度的數值模擬與可視化分析技術，深入探討微粒子在特定製程環境下的沉積行為、物理機制及其關鍵影響因素。在研究方法上，本文採用計算流體力學(CFD)技術構建三維數值模型。流場求解核心基於Navier-Stokes Equations以確保流體質量與動量的守恆；針對微粒子動力學，本研究進一步引入 Maxey-Riley Equations，藉以精確描述粒子在非均勻流場中所受之慣性力及相關流體動力耦合效應；同時配合純量傳輸方程式(Scalar Transport Equation)計算粒子之空間濃度分佈場。最後，透過 ParaView 可視化軟體，將抽象的氣流數據轉化為具象的粒子運動軌跡與速度向量圖，進而捕捉粒子在流場中的非線性運動特性。研究結果顯示，本研究之數值模擬預測結果與廠端實際監測位置之熱點數據具有高度一致性，有效驗證了模擬模型的可靠性。分析發現，粒子沉積主要集中於自動化設備周邊及氣流轉折之滯留死角。此類區域因平均流速趨近於零，形成局部迴流結構。在這種環境下，粒子因缺乏主流氣流之動能帶動，極易受重力沉降效應，導致粒子在設備表面發生大規模堆積，進而演變為製程中的二次污染源。本研究之貢獻在於，不僅釐清了高科技廠室內環境中粒子沉積的物理機制，透過局部氣流組織的調整與流場參數的變化，能發現低速區的空氣交換效率以及粒子沉積率。本研究成果可作為高科技產業未來在無塵室設計優化、製程良率提升及環境監測佈點規劃上之重要參考依據。

This study investigates internal flow dynamics and particle transport mechanisms in high-tech cleanrooms through high-fidelity numerical simulations. By analyzing the interaction between Fan Filter Unit (FFU) air distribution and complex equipment layouts, the research elucidates the physical factors governing particle deposition in semiconductor and LCD manufacturing environments.

The methodology integrates the Building Cube Method (BCM) with Large Eddy Simulation (LES) and the Immersed Boundary Method (IBM) to precisely model intricate machinery geometries. By solving the Maxey-Riley and Scalar Transport equations, the platform captures non-linear particle trajectories within non-uniform fields. The reliability of this approach is validated by the high consistency between predicted deposition hotspots and actual manufacturing site monitoring data.

Results indicate that particle accumulation is concentrated in stagnation zones and high-vorticity regions induced by equipment interference. In these areas of low velocity and turbulence intensity, particles lack the kinetic energy to be exhausted by the mainstream, becoming susceptible to boundary layer effects and diffusion. These findings underscore the critical importance of localized airflow organization in contamination control.

Beyond diagnostics, this research provides a vital dataset for developing AI-driven facility management systems. Training machine learning models on these simulated trajectories enables inverse source identification, allowing for real-time contamination detection and proactive airflow adjustment. This work serves as a key reference for improving process yields and advancing toward intelligent, self-optimizing cleanroom environments.

[1]SGS 台灣檢驗科技股份有限公司, "無塵室到底在控什麼？一次看懂無塵室為何是高科技製程的關鍵防線," SGS 技術專區, 2026, Feb. 26. [Online]. Available: https://eecloud.sgs.com/SEMI_TW/Article.aspx?n=39. [Accessed: May 17, 2026].
[2]K. C. Noh, M. D. Oh, and S. C. Lee, "A numerical study on airflow and dynamic cross-contamination in the super cleanroom for photolithography process," Building and Environment, vol. 40, no. 11, pp. 1431-1440, 2005.
[3]Hu, S. C., & Shiue, A. (2006). "Numerical and experimental study of airflow characteristics in a semiconductor cleanroom," Building and Environment, 41(12), 1627-1635.
[4]Z. Zhang and Q. Chen, "Experimental measurements and numerical simulations of particle transport and distribution in ventilated rooms," Atmospheric Environment, vol. 40, no. 18, pp. 3390–3408, 2006.
[5]Z. Liu, M. Zhang, G. Cao, S. Tang, H. Liu, and L. Wang, "Influence of air supply velocity and room temperature conditions on bioaerosols distribution in a class I operating room," Building and Environment, vol. 205, p. 108116, 2021.
[6]W.-S. Fu, C.-G. Li, W.-F. Lin, and Y.-H. Chen, "Roe scheme with preconditioning method for large eddy simulation of compressible turbulent channel flow," International Journal for Numerical Methods in Fluids, vol. 61, no. 8, pp. 888–910, 2009.
[7]W.-S. Fu, C.-G. Li, C.-P. Huang, and J.-C. Huang, "An investigation of a high temperature difference natural convection in a finite length channel without Boussinesq assumption," International Journal of Heat and Mass Transfer, vol. 52, no. 11–12, pp. 2571–2580, 2009.
[8]C.-G. Li, M. Tsubokura, and J. Onishi, "Feasibility investigation of compressible direct numerical simulation with a preconditioning method at extremely low Mach numbers," International Journal of Computational Fluid Dynamics, vol. 28, no. 6–10, pp. 411–419, 2014.
[9]J.D. Anderson, Hypersonic and high-temperature gas dynamics. 2nd ed. AIAA education series. Reston, VA: American Institute of Aeronautics and Astronautics, 2006.
[10]M. R. Maxey and J. J. Riley, "Equation of motion for a small rigid sphere in a nonuniform flow," Physics of Fluids, vol. 26, no. 4, pp. 883–889, 1983.
[11]W. C. Hinds, Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles, 2nd ed. New York: John Wiley & Sons, 1999.
[12]E. Cunningham, "On the velocity of steady fall of spherical particles through fluid medium," Proceedings of the Royal Society of London. Series A, vol. 83, no. 563, pp. 357–365, 1910.
[13]C. N. Davies, "Definitive equations for the fluid resistance of spheres," Proceedings of the Physical Society, vol. 57, no. 4, p. 259, 1945.
[14]R. Bale, A. Iida, M. Yamakawa, C. G. Li, and M. Tsubokura, "Quantifying the COVID-19 infection risk due to droplet/aerosol inhalation," Scientific Reports, vol. 12, no. 1, p. 11025, 2022.
[15]F. F. Grinstein, L. G. Margolin, and W. J. Rider, Eds., Implicit Large Eddy Simulation: Computing Turbulent Fluid Dynamics, Cambridge: Cambridge University Press, 2007.
[16]C. Fureby and F. F. Grinstein, "Monotonically integrated large eddy simulation of free shear flows," AIAA Journal, vol. 37, no. 5, pp. 544–556, 1999.
[17]O. Tetens, "Über einige meteorologische Begriffe," Zeitschrift für Geophysik, vol. 6, pp. 297–309, 1930.
[18]F. W. Murray, "On the computation of saturation vapor pressure," Journal of Applied Meteorology and Climatology, vol. 6, no. 1, pp. 203–204, 1967.
[19]R. R. Rogers and M. K. Yau, A Short Course in Cloud Physics, 3rd ed. Oxford: Elsevier, 1989.
[20]S. B. Pope, Turbulent Flows, Cambridge: Cambridge University Press, 2000.
[21]S. Murakami, S. Kato, and Y. Suyama, "Numerical and experimental study on room airflow and contaminant diffusion," ASHRAE Transactions, vol. 94, pp. 1328–1338, 1988.
[22]Q. Chen, "Ventilation performance prediction for buildings: A method overview and recent applications," Building and Environment, vol. 44, no. 4, pp. 848–858, 2009.
[23]C.-G. Li, R. Bale, W.-H. Wang, and M. Tsubokura, "A sharp interface immersed boundary method for thin-walled geometries in viscous compressible flows," International Journal of Mechanical Sciences, vol. 253, p. 108401, 2023.
[24]K. Komatsu, T. Soga, R. Egawa, H. Takizawa, H. Kobayashi, S. Takahashi, D. Sasaki, and K. Nakahashi, "Parallel processing of the Building-Cube Method on a GPU platform," Computers & Fluids, vol. 45, no. 1, pp. 122–128, 2011.
[25]K. Nakahashi and L. Kim, "Building-Cube Method for large-scale, high-resolution flow computations," AIAA Paper, 2004-0426, 2004.
[26]R. Ghias, R. Mittal, and H. Dong, "A sharp interface immersed boundary method for compressible viscous flows," Journal of Computational Physics, vol. 225, no. 1, pp. 528–553, 2007.
[27]M. D. de Tullio, P. De Palma, G. Iaccarino, G. Pascazio, and M. Napolitano, "An immersed boundary method for compressible flows using local grid refinement," Journal of Computational Physics, vol. 225, no. 2, pp. 2098–2117, 2007.
[28]P. L. Roe, "Approximate Riemann solvers, parameter vectors, and difference schemes," Journal of Computational Physics, vol. 43, no. 2, pp. 357–372, 1981.
[29]S. Yoon and A. Jameson, "Lower-upper Symmetric-Gauss-Seidel method for the Euler and Navier-Stokes equations," AIAA Journal, vol. 26, no. 9, pp. 1025–1026, 1988.
[30]G. V. Candler, M. J. Wright, and J. D. McDonald, "Data-parallel lower-upper relaxation method for reacting flows," AIAA Journal, vol. 32, no. 12, pp. 2380–2386,1994.
[31]W. Yuan and C. B. Allen, "An evaluation of implicit schemes for the solution of the incompressible Navier–Stokes equations on structured grids," International Journal for Numerical Methods in Fluids, vol. 47, no. 12, pp. 1417–1437, 2005.