Artificial Intelligence & Machine Learning | | in the Journal of Vacuum Science & Technology | | |
The Journal of Vacuum Science & Technology (JVST A & B) is proud to highlight a rapidly expanding frontier: the transformative integration of Artificial Intelligence and Machine Learning (AI & ML) within vacuum science, thin films, materials, plasma physics, and nanofabrication.
From the development of neural-network models for plasma-surface interactions to the use of Generative Adversarial Networks (GANs) for optimizing turbomolecular pump structures, our recent publications below demonstrate how AI and ML are accelerating discovery and precision.
Whether it is on-the-fly RHEED analysis during thin film deposition or attention-based Convolutional Neural Networks for silicon microstructure characterization, JVST is becoming the home for researchers leveraging AI and ML in optimizing and discovering new materials, processes, devices, and fabrication workflows.
We invite you to explore these cutting-edge studies below and join the growing community of innovators redefining the limits of what is possible in the vacuum and microelectronics industries by submitting your own work on this exciting frontier!
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- SEE RECENT JVST A ARTICLES -
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Applications of machine learning in ion beam analysis of materials
by Tiago Fiorini da Silva, J. Vac. Sci. Technol. A 43, 020803 (2025)
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Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy
by Tiffany C. Kaspar, Sarah Akers, Henry W. Sprueill, Arman H. Ter-Petrosyan, Jenna A. Bilbrey, Derek Hopkins, Ajay Harilal, Jijo Christudasjustus, Patrick Gemperline, and Ryan B. Comes, J. Vac. Sci. Technol. A 43, 032702 (2025)
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Improvement of data analytics techniques in reflection high-energy electron diffraction to enable machine learning
by Patrick T. Gemperline, Rajendra Paudel, Rama K. Vasudevan, and Ryan B. Comes, J. Vac. Sci. Technol. A 43, 032701 (2025)
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Case study in machine learning for predicting moderate pressure plasma behavior
by Shadhin Hussain, David J. Lary, Kentaro Hara, Kallol Bera, Shahid Rauf, and Matthew Goeckner, J. Vac. Sci. Technol. A 42, 043001 (2024)
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Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine
by Andreas Kounis-Melas, Athanassios Z. Panagiotopoulos, and David B. Graves, J. Vac. Sci. Technol. A 43, 063204 (2025)
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Artificial intelligence in atomic layer deposition
by Pouyan Navabi, Remya Ampadi Ramachandran, Harshdeep Bhatia, Majid Jaberi-Douraki, Urmila Diwekar, Cortino Sukotjo, and Christos G. Takoudis, J. Vac. Sci. Technol. A 43, 060801 (2025)
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Optimizing impedance matching parameters for single-frequency capacitively coupled plasma via machine learning
by Dehen Cao, Shimin Yu, Zili Chen, Yu Wang, Hongyu Wang, Zhipeng Chen, Wei Jiang, Ya Zhang, J. Vac. Sci. Technol. A 42, 013001 (2024)
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Development and transferability of neural-network models for plasma-surface interactions
by J. S. Draney, A. Z. Panagiotopoulos, and D. B. Graves, J. Vac. Sci. Technol. A 43, 063002 (2025)
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- SEE RECENT JVST B ARTICLES -
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Predicting global warming potential: A self-training and ensemble machine learning approach
by Sungyong Park, Sung-Woo Park, and Jinhee Lee, J. Vac. Sci. Technol. B 43, 064004 (2025)
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Machine learning aided optimization of drag pump performance
by Wenbi Zhou, Jianxiong Pan, Cheng Cheng, Chenghui Pang, and Dongjun Jiang, J. Vac. Sci. Technol. B 43, 054203 (2025)
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Machine learning driven measurement of high-aspect-ratio nanostructures using Mueller matrix spectroscopic ellipsometry
by Shiva Mudide, Nick Keller, G. Andrew Antonelli, Geraldina Cruz, Julia Hart, Alexander R. Bruccoleri, Ralf K. Heilmann, and Mark L. Schattenburg, J. Vac. Sci. Technol. B 43, 012801 (2025)
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Optimization of turbomolecular pump blade structure based on generative adversarial network
by X. Wang, X. Lei, Z. Ge, B. Han, and X. Wang, J. Vac. Sci. Technol. B 44, 014204 (2026)
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SiMiC: Context-aware silicon microstructure characterization using attention-based convolutional neural networks for field-emission tip analysis
by Jing Jie Tan, Rupert Schreiner, Matthias Hausladen, Ali Asgharzade, Simon Edler, Julian Bartsch, Michael Bachmann, Andreas Schels, Ban-Hoe Kwan, Danny Wee-Kiat Ng, and Yan-Chai Hum, J. Vac. Sci. Technol. B 43, 064205 (2025)
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