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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.
Area-selective atomic layer deposition using small-molecule inhibitors (SMIs) involves vapor-phase dosing of inhibitor molecules, resulting in an industry-compatible approach. However, the identification of suitable SMIs that yield a high selectivity remains a challenging task. Recently, aniline (C 6 H 5 NH 2 ) was shown to be an effective SMI during the area-selective deposition (ASD) of TiN, giving 6 nm of selective growth on SiO 2 in the presence of Ru and Co non-growth areas. In this work, using density functional theory (DFT) and random sequential adsorption (RSA) simulations, we investigated how aniline can effectively block precursor adsorption on specific areas. Our DFT calculations confirmed that aniline selectively adsorbs on Ru and Co non-growth areas, whereas its adsorption on the SiO 2 growth area is limited to physisorption. DFT reveals two stable adsorption configurations of aniline on the metal surfaces. Further calculations on the aniline-functionalized surfaces show that the aniline inhibitor significantly reduces the interaction of Ti precursor, tetrakis(dimethylamino)titanium, with the non-growth area. In addition, RSA simulations showed that the co-presence of two stable adsorption configurations allows for a high surface inhibitor coverage on both Co and Ru surfaces. As the surface saturates, there is a transition from the thermodynamically most favorable adsorption configuration to the sterically most favorable adsorption configuration, which results in a sufficiently dense inhibition layer, such that an incoming precursor molecule cannot fit in between the adsorbed precursor molecules. We also found that, as a result of the catalytic activity of the metallic non-growth area, further reactions of inhibitor molecules, such as hydrogenolysis, can play a role in precursor blocking.
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