Artificial intelligence in atomic layer deposition

2025/10/16 by Pouyan Navabi, Remya Ampadi Ramachandran, Harshdeep Bhatia +4 · 1 voice · 1 citation
Engineering · Materials Science · #Semiconductor materials and devices #Electronic and Structural Properties of Oxides #Advanced Memory and Neural Computing

paper · doi:10.1116/6.0004881

openalex created_date 2025/10/16 · openalex publication_date 2025/10/16 · openalex updated_date 2026/07/22

Abstract

Atomic layer deposition (ALD) is a vapor-phase thin-film deposition technique offering precise atomic-scale control over film thickness and conformality. Widely used across diverse fields including microelectronics, energy storage, and biomaterials, traditional optimization of ALD processes typically relies on trial-and-error experimentation or computational simulations. Recently, artificial intelligence (AI) and machine learning (ML) methods have emerged as powerful tools to significantly accelerate and improve ALD process optimization, material discovery, and fundamental understanding of ALD chemistry. In this review, we highlight recent advancements in the intersection of ALD and AI/ML, examining their roles in process optimization, new material development, reaction mechanism exploration, and the application of advanced algorithms, including large language models. Additionally, we introduce a bibliometric approach to systematically collect and analyze relevant scientific literature. Finally, we outline existing challenges and provide insights into the future potential of integrating AI with ALD to drive further innovation in materials science and engineering.

Citations

• Assessing the Environmental Impact of Atomic Layer Deposition (ALD) Processes and Pathways to Lower It
• Can Large Language Models (LLMs) Act as Virtual Safety Officers?
• Recent trends in thermal atomic layer deposition chemistry
• Benchmarking large language models for materials synthesis: the case of atomic layer deposition

Cited by

• Tuning of atomic layer deposition pulse time through physics-informed Bayesian active learning

Discussions

• AI & ML improve #ALDep by optimizing processes, predicting film quality, assisting precursor discovery, & integrating physics-informed models for reliable thin-film design. doi.org/10.1116/6.00... [bsky, 0 points, 0 comments]

Related

• Atomic Layer Deposition
• Performance of AI agents based on reasoning language models on ALD process optimization tasks
• A Machine Learning Approach Capturing Hidden Parameters in Autonomous Thin-Film Deposition
• Surrogate models to optimize plasma assisted atomic layer deposition in high aspect ratio features
• Benchmarking large language models for materials synthesis: the case of atomic layer deposition