Atomic layer deposition (ALD) is a precise thin-film fabrication technique that relies on self-limiting surface reactions to achieve atomic-scale control over film thickness and composition. The optimization of ALD parameters, such as temperature, precursor pulse times, purge durations, and reactant exposure, directly influences film properties like uniformity, conformality, stoichiometry, and crystallinity. Machine learning (ML) models have emerged as powerful tools to accelerate the optimization process by identifying complex relationships between process parameters and film characteristics without exhaustive experimental iterations.

The application of ML in ALD parameter optimization typically involves supervised learning frameworks, where models are trained on datasets comprising process conditions and corresponding film properties. These datasets are generated through experimental runs or derived from physics-based simulations. Regression models, such as Gaussian process regression (GPR), support vector regression (SVR), and artificial neural networks (ANNs), are commonly employed to predict film properties based on input parameters.

Gaussian process regression is particularly effective for ALD optimization due to its ability to handle small datasets and provide uncertainty estimates. GPR models the relationship between ALD parameters and film properties as a probabilistic distribution, allowing researchers to assess prediction confidence. For example, a GPR model trained on temperature and pulse time data for aluminum oxide (Al2O3) ALD can predict film growth per cycle (GPC) with high accuracy while quantifying the uncertainty associated with unexplored parameter combinations. This enables efficient exploration of the parameter space, guiding experimentalists toward optimal conditions with minimal trials.

Support vector regression is another robust algorithm for ALD optimization, especially when dealing with nonlinear relationships between parameters and film properties. SVR maps input variables into a high-dimensional feature space where linear regression is performed, effectively capturing complex dependencies. In one study, an SVR model was trained to predict the refractive index and thickness of titanium dioxide (TiO2) films deposited via ALD by correlating these properties with precursor pulse times and substrate temperature. The model achieved a mean absolute error of less than 2% compared to experimental measurements, demonstrating its utility in fine-tuning process conditions for desired optical properties.

Artificial neural networks, particularly multilayer perceptrons (MLPs) and convolutional neural networks (CNNs), excel in handling high-dimensional ALD datasets. ANNs can model intricate interactions between multiple parameters, such as co-reactant ratios, purge steps, and deposition temperature, to predict film characteristics like roughness or electrical conductivity. For instance, an ANN trained on zinc oxide (ZnO) ALD data successfully predicted crystallinity transitions as a function of temperature and precursor exposure, enabling precise control over film phase behavior.

Reinforcement learning (RL) has also been applied to ALD optimization, where an agent iteratively adjusts process parameters to maximize a reward function tied to film quality. In one implementation, an RL algorithm optimized the pulse sequence for hafnium oxide (HfO2) ALD, minimizing impurities while maintaining a target growth rate. The algorithm explored parameter combinations through simulated deposition cycles, converging on an optimal recipe faster than traditional design-of-experiments approaches.

Feature selection techniques are critical in ML-driven ALD optimization to identify the most influential parameters. Methods like principal component analysis (PCA) and recursive feature elimination (RFE) reduce dimensionality by ranking parameters based on their impact on film properties. For example, PCA applied to a dataset of silicon nitride (Si3N4) ALD revealed that purge time and plasma power were dominant factors affecting film stoichiometry, allowing researchers to focus adjustments on these variables.

Cross-validation is essential to ensure ML model generalizability. K-fold cross-validation divides the dataset into training and validation subsets, preventing overfitting and verifying prediction robustness. A study on tungsten (W) ALD used 5-fold cross-validation to confirm that an ANN model maintained high accuracy across different deposition regimes, ensuring reliable predictions for unseen process conditions.

Despite their advantages, ML models for ALD optimization face challenges related to data scarcity and noise. ALD experiments are time-consuming, limiting dataset size. Transfer learning addresses this by leveraging pre-trained models from related deposition processes or synthetic data generated from kinetic simulations. For example, a model initially trained on Al2O3 ALD data was fine-tuned with limited experimental results for gallium oxide (Ga2O3), reducing the need for extensive new data collection.

Real-time ML integration with ALD systems is an emerging trend. Closed-loop control systems use ML models to adjust parameters dynamically during deposition based on in-situ sensor feedback. Optical emission spectroscopy (OES) and quartz crystal microbalance (QCM) data can be streamed into ML algorithms to correct deviations from target film properties mid-process. In one demonstration, a neural network adjusted trimethylaluminum (TMA) and water pulse times in real-time to maintain consistent Al2O3 growth rates despite chamber conditioning effects.

The future of ML in ALD optimization lies in hybrid models that combine data-driven approaches with physical principles. Physics-informed neural networks (PINNs) incorporate known ALD reaction kinetics into ML architectures, improving extrapolation beyond the training dataset. For instance, a PINN for platinum (Pt) ALD integrated Arrhenius-type equations for precursor adsorption, enhancing predictions at temperatures outside the experimental range.

In summary, machine learning accelerates ALD parameter optimization by uncovering hidden relationships between process variables and film properties. Regression models, reinforcement learning, and real-time adaptive systems enable precise control over deposition outcomes while minimizing experimental overhead. As datasets grow and algorithms advance, ML-driven ALD will become increasingly integral to thin-film engineering for semiconductors, energy storage, and functional coatings.