Static Atomic Layer Deposition for Enhanced Precursor Infiltration in Porous Materials: A Combined Experimental and Numerical Analysis

Abstract

Atomic layer deposition (ALD) is widely used to coat porous materials, but conventional continuous-flow ALD often suffers from limited infiltration depth and inefficient precursor utilization in high-aspect-ratio pore networks. To address these limitations, we investigated static ALD, a modified process that introduces an additional closed-valve duration step between precursor injection and purge. This step prolongs precursor residence time, promoting deeper infiltration and greater precursor utilization, especially for low-reactivity precursors. Static ALD was evaluated using diethylzinc (DEZ) and tetraethoxysilane (TEOS) as representative precursors with contrasting sticking coefficients. To interpret the experimental behavior, we extended a continuum diffusion–reaction model by incorporating a dynamic boundary condition that accounts for progressive precursor depletion in a closed-valve chamber under finite-supply conditions. Combined experimental and numerical results show that static ALD yields significantly greater mass gain and deeper infiltration than conventional ALD at a fixed accumulated injection time for each precursor, particularly for low-reactivity precursors such as TEOS. Parametric simulations further reveal that the optimal duration time depends strongly on precursor reactivity, as low-reactivity precursors require longer residence times for efficient infiltration. Surface coverage profiles are governed primarily by the sticking coefficient, whereas process configuration determines the achievable infiltration depth. Overall, this study establishes a modeling-guided framework for tailoring static ALD to precursor properties and substrate geometries, enabling more efficient and scalable ALD in porous nanostructures.