Speaker
Description
This talk will present the key elements of the computational multiscale modelling approach to simulating 3D nanofabrication using focused electron beam-induced deposition (FEBID) [1-5]. This approach is based on computational algorithms (Irradiation-Driven Molecular Dynamics [2] and Stochastic Dynamics – SD [5,6]) implemented in the advanced software package MBN Explorer [7], which is being developed by the MBN Research Center in Frankfurt (https://www.mbnresearch.com/).
The talk will focus specifically on our recent SD simulation results for nanopillar growth using FEBID [5,8]. The SD method uses probabilistic theory to describe the FEBID process, involving particles that represent intact precursor molecules, their fragments, ligands, and the substrate [5,7]. This modelling approach incorporates a detailed description of elementary processes, including precursor adsorption, diffusion, desorption, dissociation, and the growth of metal-containing deposits. As an illustrative case study, we have analysed the growth of nanopillars using the FEBID of W(CO)$_6$ precursors on a SiO$_2$ substrate under 30 keV electron beam irradiation. The simulation protocol accounts for realistic irradiation/replenishment cycles, precursor injection flux, and fragmentation rates, which are derived from track-structure Monte Carlo simulations [5].
The simulation results are systematically validated against relevant experimental data [9] in terms of deposit’s composition, size and growth rate. Importantly, the simulations provide a detailed characterisation of the deposit’s structure at a nanoscopic level. The utilized multiscale modelling approach provides a robust foundation for predictive simulations of irradiation-driven fabrication processes and their applications in FEBID-based 3D-nanoprinting.
The authors acknowledge the support received through the COST Innovators Grant project IG20129 INDICO, which is supported by COST (European Cooperation in Science and Technology).
References:
[1] A.V. Solov’yov et al., Chem. Rev. 124 (2024) 8014-8129
[2] G.B. Sushko, I.A. Solov’yov, A.V. Solov’yov, Eur. Phys. J. D 70 (2016) 217
[3] P. de Vera, M. Azzolini, G.B. Sushko, I. Abril, I., R. Garcia-Molina, M. Dapor, I.A. Solov’yov, A.V. Solov’yov, Sci. Rep. 10 (2020) 20827
[4] A. Prosvetov, A.V. Verkhovtsev, G. Sushko, A.V. Solov’yov, Phys. Chem. Chem. Phys. 24 (2022) 10807
[5] I.A. Solov’yov, A. Prosvetov, G. Sushko, A.V. Solov’yov, https://arxiv.org/abs/2506.18163 (2025)
[6] I.A. Solov’yov, G. Sushko, I. Friis, A.V. Solov’yov, J. Comput. Chem. 43 (2022) 1442
[7] I.A. Solov’yov, A.V. Yakubovich, P.V. Nikolaev, I. Volkovets, and A.V. Solov’yov, J. Comput. Chem. 33 (2012) 2412
[8] A.V. Verkhovtsev, G. Sushko, J. Kornblueh, I.A. Solov’yov, A.V. Solov’yov (in preparation, 2026)
[9] J.D. Fowlkes, P.D. Rack, ACS Nano 4 (2010) 1619