Area-selective atomic layer deposition (ALD) is of interest for applications in self-aligned processing of nanoelectronics. Selective deposition is generally enabled by functionalization of the area where no growth is desired with inhibitor molecules. The packing of these inhibitor molecules, in terms of molecule arrangement and surface density, plays a vital role in deactivating the surface by blocking the precursor adsorption. In this work, we performed random sequential adsorption (RSA) simulations to investigate the packing of small molecule inhibitors (SMIs) on a surface in order to predict how effective the SMI blocks precursor adsorption. These simulations provide insight into how the packing of inhibitor molecules depends on the molecule size, molecule shape, and their ability to diffuse over the surface. Based on the RSA simulations, a statistical method was developed for analyzing the sizes of the gaps in between the adsorbed inhibitor molecules, serving as a quantitative parameter on the effectiveness of precursor blocking. This method was validated by experimental studies using several alcohol molecules as SMIs in an area-selective deposition process for SiO2. It is demonstrated that RSA simulations provide an insightful and straightforward method for screening SMIs in terms of their potential for area-selective ALD.
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See supplementary material at https://www.scitation.org/doi/suppl/10.1116/6.0002096 for additional DFT results and the IR spectra showing ethanol displacement.
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