Supervised Learning Frameworks in ALD Optimization

Atomic layer deposition (ALD) relies on self‑limiting surface reactions to control film thickness and composition at the atomic scale. Machine learning (ML) accelerates the identification of optimal process parameters—temperature, precursor pulse times, purge durations, and reactant exposure—by modeling complex nonlinear relationships between inputs and film properties such as uniformity, conformality, stoichiometry, and crystallinity.

Gaussian Process Regression (GPR)

Gaussian process regression is well suited for ALD because it handles small experimental datasets and provides probabilistic uncertainty estimates. A GPR model trained on temperature and pulse time data for aluminum oxide (Al₂O₃) films predicts growth per cycle (GPC) with high accuracy and quantifies confidence in unexplored parameter regions. This probabilistic guidance allows researchers to efficiently navigate the parameter space with minimal experimental iterations.

Support Vector Regression (SVR)

Support vector regression captures nonlinear dependencies by mapping input variables into a high‑dimensional feature space. In a study on titanium dioxide (TiO₂) ALD, an SVR model correlated precursor pulse times and substrate temperature with refractive index and film thickness. The model achieved a mean absolute error of less than 2% compared to experimental measurements, demonstrating robust predictive capability for optical property tuning.

Artificial Neural Networks (ANNs)

Multilayer perceptrons and convolutional neural networks model high‑dimensional interactions among parameters such as co‑reactant ratios, purge steps, and deposition temperature. For zinc oxide (ZnO) ALD, an ANN predicted crystallinity transitions as functions of temperature and precursor exposure, enabling precise control over film phase behavior. Another ANN for tungsten (W) ALD maintained high accuracy across different deposition regimes, as confirmed by 5‑fold cross‑validation.

ML Model	Typical Application	Reported Performance
Gaussian Process Regression	Al₂O₃ GPC prediction	High accuracy with probabilistic bounds
Support Vector Regression	TiO₂ refractive index & thickness	Mean absolute error < 2%
Artificial Neural Network	ZnO crystallinity, W uniformity	Accurate across regimes (5‑fold CV)

Reinforcement Learning for Parameter Search

Reinforcement learning (RL) treats ALD optimization as a sequential decision‑making problem. An RL agent adjusts process parameters iteratively 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 agent explored parameter combinations through simulated deposition cycles and converged on an optimal recipe faster than traditional design‑of‑experiments methods.

Data Handling and Feature Engineering

Limited experimental data is a key challenge in ALD. Feature selection techniques such as principal component analysis (PCA) and recursive feature elimination (RFE) reduce dimensionality by ranking parameters according to their influence on film properties. PCA of a silicon nitride (Si₃N₄) ALD dataset identified purge time and plasma power as dominant factors affecting stoichiometry, allowing researchers to focus adjustments on these variables.

Cross‑Validation for Generalization

K‑fold cross‑validation prevents overfitting and ensures model robustness. A study on tungsten ALD used 5‑fold cross‑validation to verify that an ANN model retained high predictive accuracy across unseen deposition conditions, confirming reliable generalization.

Transfer Learning to Overcome Data Scarcity

Transfer learning leverages pre‑trained models from related processes or synthetic data generated from kinetic simulations. 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 while preserving predictive performance.

Real‑Time Integration with ALD Systems

Closed‑loop control systems integrate ML models with in‑situ sensors such as optical emission spectroscopy (OES) and quartz crystal microbalance (QCM). In a demonstration, a neural network adjusted trimethylaluminum (TMA) and water pulse times in real time to maintain consistent Al₂O₃ growth rates despite chamber conditioning effects. This approach reduces process drift and enhances reproducibility.

Hybrid Models Combining Physics and Data

Physics‑informed neural networks (PINNs) embed known ALD reaction kinetics—such as Arrhenius‑type equations for precursor adsorption—into the ML architecture. A PINN for platinum (Pt) ALD improved predictions at temperatures outside the training range by incorporating physical constraints, enabling extrapolation beyond the experimental dataset.

Future Directions and Challenges

While ML accelerates ALD optimization, data scarcity and measurement noise remain obstacles. Advances in transfer learning, synthetic data generation, and hybrid physics‑informed models promise to enhance robustness. As datasets grow and algorithms mature, ML‑driven ALD will become a standard tool for thin‑film engineering in semiconductors, energy storage, and functional coatings.