Atomic layer deposition (ALD) relies on alternated, self-limiting reactions between gaseous reactants and an exposed solid surface to deposit highly conformal coatings with a thickness controlled at the submonolayer level. These advantages have rendered ALD a mainstream technique in microelectronics and have triggered growing interest in ALD for a variety of nanotechnology applications, including energy technologies. Often, the choice for ALD is related to the need for a conformal coating on a 3D nanostructured surface, making the conformality of ALD processes a key factor in actual applications. In this work, we aim to review the current status of knowledge about the conformality of ALD processes. We describe the basic concepts related to the conformality of ALD, including an overview of relevant gas transport regimes, definitions of exposure and sticking probability, and a distinction between different ALD growth types observed in high aspect ratio structures. In addition, aiming for a more standardized and direct comparison of reported results concerning the conformality of ALD processes, we propose a new concept, Equivalent Aspect Ratio (EAR), to describe 3D substrates and introduce standard ways to express thin film conformality. Other than the conventional aspect ratio, the EAR provides a measure for the ease of coatability by referring to a cylindrical hole as the reference structure. The different types of high aspect ratio structures and characterization approaches that have been used for quantifying the conformality of ALD processes are reviewed. The published experimental data on the conformality of thermal, plasma-enhanced, and ozone-based ALD processes are tabulated and discussed. Besides discussing the experimental results of conformality of ALD, we will also give an overview of the reported models for simulating the conformality of ALD. The different classes of models are discussed with special attention for the key assumptions typically used in the different modelling approaches. The influence of certain assumptions on simulated deposition thickness profiles is illustrated and discussed with the aim of shedding light on how deposition thickness profiles can provide insights into factors governing the surface chemistry of ALD processes. We hope that this review can serve as a starting point and reference work for new and expert researchers interested in the conformality of ALD and, at the same time, will trigger new research to further improve our understanding of this famous characteristic of ALD processes.

/pdf/conformality-in-atomic-layer-deposition-current-status-3xfw4mj602.pdf

Conformality in atomic layer deposition : current status overview of analysis and modelling

One-dimensional Anodic TiO2 Nanotubes Coated by Atomic Layer Deposition: Towards Advanced Applications

The conformality of thin films grown by atomic layer deposition (ALD) is studied using all-silicon test structures with long narrow lateral channels. A diffusion model, developed in this work, is used for studying the propagation of ALD growth in narrow channels. The diffusion model takes into account the gas transportation at low pressures, the dynamic Langmuir adsorption model for the film growth and the effect of channel narrowing due to film growth. The film growth is calculated by solving the diffusion equation with surface reactions. An efficient analytic approximate solution of the diffusion equation is developed for fitting the model to the measured thickness profile. The fitting gives the equilibrium constant of adsorption and the sticking coefficient. This model and Gordon's plug flow model are compared. The simulations predict the experimental measurement results quite well for Al2O3 and TiO2 ALD processes.

/pdf/modeling-growth-kinetics-of-thin-films-made-by-atomic-layer-4dqdsmv0ot.pdf

Modeling growth kinetics of thin films made by atomic layer deposition in lateral high-aspect-ratio structures

This work develops a first-principles-based three-dimensional, multiscale computational fluid dynamics (CFD) model, together with reactor geometry optimizations, of SiO2 thin-film thermal atomic layer deposition (ALD) using bis(tertiary-butylamino)silane (BTBAS) and ozone as precursors. Specifically, an accurate macroscopic CFD model of the ALD reactor chamber gas-phase development is integrated with a detailed microscopic kinetic Monte Carlo (kMC) model that was developed in Ding et al. (2019) , accounting for the microscopic lattice structure, atomic interactions and detailed surface chemical reactions. The multiscale information exchange and the transient simulation of the microscopic distributed kMC algorithms and the macroscopic CFD model are achieved through a parallel processing message passing interface (MPI) structure. Additionally, density functional theory (DFT)-based calculations are adopted to compute the key thermodynamic and kinetic parameters for the microscopic thin-film growth process. Recognizing the transient non-uniformity and the possibility to reduce the current ALD cycle time, the optimal configuration of reactor geometry is designed and evaluated including a showerhead panel adjustment and geometry modifications on reactor inlet and upstream. It is demonstrated that with suitable reactor chamber design the required BTBAS ALD half-cycle time can be reduced by 39.6%.

/pdf/multiscale-computational-fluid-dynamics-modeling-of-thermal-4b2t52hzfw.pdf

Multiscale computational fluid dynamics modeling of thermal atomic layer deposition with application to chamber design

Atomic layer deposition in porous electrodes: A pore-scale modeling study

The authors present a new method to determine film thicknesses and sticking coefficients (SC) of precursor molecules for atomic layer deposition (ALD) in high aspect ratio three dimensional (3D) geometries as they appear in microelectromechanical system manufacturing. The method combines a specifically designed experimental test structure with the theoretical predictions from a novel 3D Monte Carlo process simulation for large structures. The authors exemplify our method using Al2O3 and SiO2 ALD processes. SCs for trimethylaluminium and bis-diethyl aminosilane (BDEAS) are extracted. The SC for BDEAS is determined for the first time.

Experimental and simulation approach for process optimization of atomic layer deposited thin films in high aspect ratio 3D structures

We present a new simulation method predicting thicknesses of thin films obtained by atomic layer deposition in high aspect ratio 3D geometries as they appear in MEMS manufacturing. The method features a Monte-Carlo computation of film deposition in free molecular flow, as well as in the Knudsen and diffusive gas regime, applicable for large structures. We compare our approach to analytic and simulation results from the literature. The capability of the method is demonstrated by a comparison to experimental film thicknesses in a large 3D structure. Finally, the feasability to extract process parameters, i.e. sticking coefficients is shown.

Simulation approach of atomic layer deposition in large 3D structures

Electronic control units (ECUs) are essential for many automobile components, e.g. engine, anti-lock braking system (ABS), steering and airbags. For some products, the 3D pose of each single ECU needs to be determined during series production. Deep learning approaches can not easily be applied to this problem, because labeled training data is not available in sufficient numbers. Thus, we train state-of-the-art artificial neural networks (ANNs) on purely synthetic training data, which is automatically created from a single CAD file. By randomizing parameters during rendering of training images, we enable inference on RGB images of a real sample part. In contrast to classic image processing approaches, this data-driven approach poses only few requirements regarding the measurement setup and transfers to related use cases with little development effort.

CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings.

CAD-to-real: enabling deep neural networks for 3D pose estimation of electronic control units

—Thermal Interface Materials (TIMs) are widely used in electronic packaging. Increasing power density and limited assembly space pose high demands on thermal management. Large cooling surfaces need to be covered efﬁciently. When joining the heatsink, previously dispensed TIM spreads over the cooling surface. Recommendations on the dispensing pattern exist only for simple surface geometries such as rectangles. For more complex geometries, Computational Fluid Dynamics (CFD) simulations are used in combination with manual experiments. While CFD simulations offer a high accuracy, they involve simulation experts and are rather expensive to set up. We propose a lightweight heuristic to model the spreading behavior of TIM. We further speed up the calculation by training an Artiﬁcial Neural Network (ANN) on data from this model. This offers rapid computation times and further supplies gradient information. This ANN can not only be used to aid manual pattern design of TIM, but also enables an automated pattern optimization. We compare this approach against the state-of-the-art and use real product samples for validation.

Jonas Barth

Papers

Experimental and simulation approach for process optimization of atomic layer deposited thin films in high aspect ratio 3D structures

Simulation approach of atomic layer deposition in large 3D structures

CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings.

CAD-to-real: enabling deep neural networks for 3D pose estimation of electronic control units

Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks