- Open Access
Origins of intrinsic stress in disordered carbon materials: A first-principles study
Phys. Rev. B 113, 134111 – Published 14 April, 2026
DOI: https://doi.org/10.1103/hgt2-8fyw
Abstract
The origin of intrinsic stress in amorphous and structurally disordered carbon films remains a fundamental challenge. Numerous previous studies have focused on deposition parameters and their relationship with stress, resulting in the hypothesis that a high -C fraction is a key factor governing compressive stress. However, the precise role of geometrical parameters in stress generation is still unknown. Here, we show that intrinsic stress is mainly dominated by two geometrical parameters: standardized bond length and density, followed by -C fraction. We found that the high -C fraction independently contributes to tensile stress, contrary to previous experimental results. A data-driven assisted investigation of various amorphous carbon models prepared using first-principles calculations quantitatively revealed the effect of each parameter by isolating their contributions. As a result, a generalized formula is proposed to correlate the intrinsic stress and fundamental structural parameters.
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References (43)
- Y. Iwamoto, Y. Hirata, R. Takamura, H. Akasaka, and N. Ohtake, Deposition phenomena of diamond-like carbon coating on inner surface of circular metal tube by nanopulse plasma chemical vapor deposition, Diamond Relat. Mater. 121, 108749 (2022).
- K. M. Krishna, Y. Nukaya, T. Soga, T. Jimbo, and M. Umeno, Solar cells based on carbon thin films, Sol. Energy Mater. Sol. Cells 65, 163 (2001).
- Y. Ohgoe, K. K. Hirakuri, H. Saitoh, T. Nakahigashi, N. Ohtake, A. Hirata, K. Kanda, M. Hiratsuka, and Y. Fukui, Classification of DLC films in terms of biological response, Surf. Coat. Technol. 207, 350 (2012).
- C.-Y. Ho, X. Lin, H. Chien, and C. Lien, High aspect ratio contact hole etching using relatively transparent amorphous carbon hard mask deposited from propylene, Thin Solid Films 518, 6076 (2010).
- M. Kakuchi, M. Hikita, and T. Tamamura, Amorphous carbon films as resist masks with high reactive ion etching resistance for nanometer lithography, Appl. Phys. Lett. 48, 835 (1986).
- S. Lee, J. Won, J. Choi, S. Jang, Y. Jee, H. Lee, and D. Byun, Preparation and analysis of amorphous carbon films deposited from C6H12/Ar/He chemistry for application as the dry etch hard mask in the semiconductor manufacturing process, Thin Solid Films 519, 6737 (2011).
- W. Liu, D. Mui, T. Lill, M. D. Wang, C. Bencher, M. Kwan, W. Yeh, T. Ebihara, and T. Oga, Generating sub-30-nm polysilicon gates using PECVD amorphous carbon as hardmask and anti-reflective coating, Opt. Microlith. XVI 5040, 841 (2003).
- J. Robertson, Diamond-like amorphous carbon, Mater. Sci. Eng.: R: Reports 37, 129 (2002).
- M. Chhowalla, J. Robertson, C. Chen, S. Silva, C. Davis, G. Amaratunga, and W. Milne, Influence of ion energy and substrate temperature on the optical and electronic properties of tetrahedral amorphous carbon (ta-C) films, J. Appl. Phys. 81, 139 (1997).
- P. J. Fallon, V. S. Veerasamy, C. A. Davis, J. Robertson, G. A. Amaratunga, W. I. Milne, and J. Koskinen, Properties of filtered-ion-beam-deposited diamondlike carbon as a function of ion energy, Phys. Rev. B 48, 4777 (1993).
- M. Polo, J. Andujar, A. Hart, J. Robertson, and W. Milne, Preparation of tetrahedral amorphous carbon films by filtered cathodic vacuum arc deposition, Diamond Relat. Mater. 9, 663 (2000).
- Y. Lifshitz, S. R. Kasi, J. W. Rabalais, and W. Eckstein, Subplantation model for film growth from hyperthermal species, Phys. Rev. B 41, 10468 (1990).
- D. R. McKenzie, D. Muller, and B. A. Pailthorpe, Compressive-stress-induced formation of thin-film tetrahedral amorphous carbon, Phys. Rev. Lett. 67, 773 (1991).
- J. Schwan, S. Ulrich, T. Theel, H. Roth, H. Ehrhardt, P. Becker, and S. Silva, Stress-induced formation of high-density amorphous carbon thin films, J. Appl. Phys. 82, 6024 (1997).
- M. Bilek and D. McKenzie, A comprehensive model of stress generation and relief processes in thin films deposited with energetic ions, Surf. Coat. Technol. 200, 4345 (2006).
- C. Davis, A simple model for the formation of compressive stress in thin films by ion bombardment, Thin Solid Films 226, 30 (1993).
- P. C. Kelires, Stress properties of diamond-like amorphous carbon, Phys. B (Amsterdam) 296, 156 (2001).
- A. Ferrari, S. Rodil, J. Robertson, and W. Milne, Is stress necessary to stabilise sp3 bonding in diamond-like carbon? Diamond Relat. Mater. 11, 994 (2002).
- A. Ferrari, B. Kleinsorge, N. Morrison, A. Hart, V. Stolojan, and J. Robertson, Stress reduction and bond stability during thermal annealing of tetrahedral amorphous carbon, J. Appl. Phys. 85, 7191 (1999).
- J. Sullivan, T. Friedmann, and A. Baca, Stress relaxation and thermal evolution of film properties in amorphous carbon, J. Electron. Mater. 26, 1021 (1997).
- M. F. Thorpe, Bulk and surface floppy modes, J. NonCryst. Solids 182, 135 (1995).
- M. M. M. Bilek, D. R. McKenzie, D. G. McCulloch, and C. M. Goringe, Ab initio simulation of structure in amorphous hydrogenated carbon, Phys. Rev. B 62, 3071 (2000).
- A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera, Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Inf. Fusion 58, 82 (2020).
- D. Minh, H. X. Wang, Y. F. Li, and T. N. Nguyen, Explainable artificial intelligence: A comprehensive review, Artif. Intell. Rev. 55, 3503 (2022).
- C. Molnar, Interpretable Machine Learning, 3rd ed. (Lulu Press, Inc., NC, USA, 2025).
- Y. Ando, H. Kondo, T. Tsutsumi, K. Ishikawa, M. Sekine, and M. Hori, Analysis of the synergetic effect of process parameters of hydrogenated amorphous carbon deposition in plasma-enhanced chemical vapor deposition using machine learning, Diamond Relat. Mater. 151, 111687 (2025).
- G. Kresse and J. Hafner, Ab initio molecular dynamics for liquid metals, Phys. Rev. B 47, 558 (1993).
- G. Kresse and J. Furthmüller, Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set, Phys. Rev. B 54, 11169 (1996).
- G. Kresse and J. Furthmüller, Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set, Comput. Mater. Sci. 6, 15 (1996).
- G. Kresse and D. Joubert, From ultrasoft pseudopotentials to the projector augmented-wave method, Phys. Rev. B 59, 1758 (1999).
- J. P. Perdew, K. Burke, and M. Ernzerhof, Generalized gradient approximation made simple, Phys. Rev. Lett. 77, 3865 (1996).
- S. Grimme, S. Ehrlich, and L. Goerigk, Effect of the damping function in dispersion corrected density functional theory, J. Comput. Chem. 32, 1456 (2011).
- H. J. Monkhorst and J. D. Pack, Special points for Brillouin-zone integrations, Phys. Rev. B 13, 5188 (1976).
- A. H. Larsen et al., The atomic simulation environment—a Python library for working with atoms, J. Phys.: Condens. Matter 29, 273002 (2017).
- L. Rao, H. Liu, T. Hu, W. Shao, Z. Shi, X. Xing, Y. Zhou, and Q. Yang, Relationship between bonding characteristic and thermal property of amorphous carbon structure: Ab initio molecular dynamics study, Diamond Relat. Mater. 111, 108211 (2021).
- A. Ito, A. Takayama, Y. Oda, and H. Nakamura, The First Principle Calculation of Bulk Modulus and Young's Modulus for Amorphous Carbon Material, J. Phys. Conf. Ser. 518, 012011 (2014).
- E. Kiely, R. Zwane, R. Fox, A. M. Reilly, and S. Guerin, Density functional theory predictions of the mechanical properties of crystalline materials, CrystEngComm 23, 5697 (2021).
- S. Nosé, A unified formulation of the constant temperature molecular dynamics methods, J. Chem. Phys. 81, 511 (1984).
- S. Nosé, Constant temperature molecular dynamics methods, Prog. Theor. Phys. Suppl. 103, 1 (1991).
- W. G. Hoover, Canonical dynamics: Equilibrium phase-space distributions, Phys. Rev. A 31, 1695 (1985).
- C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (The MIT Press, Cambridge, MA, 2005).
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, Scikit-learn: Machine learning in python, J. Mach. Learn. Res. 12, 2825 (2011).
- S. M. Lundberg and S.-I. Lee, A unified approach to interpreting model predictions, in NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems (ACM, New York, NY, 2017), PP. 4768–4777.