Bulletin of the American Physical Society
75th Annual Gaseous Electronics Conference
Volume 67, Number 9
Monday–Friday, October 3–7, 2022;
Sendai International Center, Sendai, Japan
The session times in this program are intended for Japan Standard Time zone in Tokyo, Japan (GMT+9)
Session EW5: Plasma Surface Interaction II |
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Chair: Summit Agarwal, Colorado School of Mines Room: Sendai International Center Hagi |
Wednesday, October 5, 2022 2:30PM - 2:45PM |
EW5.00001: A global plasma and surface model of a hydrogen/methane inductively coupled discharges for the purpose of minimal optical transmission loss in Extreme-Ultra-Violet lithography machines Efe Kemaneci, Achim von Keudell, Andrei Yakunin, Andrey Nikipelov, Mark van de Kerkhof, Vadim Banine The Extreme-Ultra-Violet (EUV) lithography is of the essence to extend the limits of the Moore’s law in the modern integrated circuit technology. Hydrocarbon growth on the interior surfaces of the lithography machines, exposed to EUV-induced plasma, is one of the obstacles for sufficient optical lifetime. A global model of inductive discharges with 469 homogeneous gas phase reactions, coupled with a surface deposition/etch model of 42 heterogeneous surface reactions, is developed for a feeding gas of hydrogen mixed with methane to investigate the plasma-surface interaction in lithography machines. The simulation results are validated against a wide variety of measurements such as electron density, electron temperature and hydrogen atom density. |
Wednesday, October 5, 2022 2:45PM - 3:00PM |
EW5.00002: Implementation of Interatomic Potential for Charged Particle Collision Yuto Toda, Arimichi Takayama, Atsushi M Ito Binary collision approximation (BCA) is the simulation method that deals with high-energy collisions such as plasma-material interactions. It is important to select appropriate interatomic potential models for that simulation. The Ziegler-Biersack-Littmark (ZBL) potential model has been often used in BCA calculations. |
Wednesday, October 5, 2022 3:00PM - 3:30PM |
EW5.00003: Machine learning plasma-surface interactions: from low to high fidelity surrogate models Invited Speaker: Jan Trieschmann Many technological applications of low-temperature plasmas (LTPs) rely on the interaction of the plasma with the surrounding walls. Whereas plasma-surface interactions (PSIs) may be described by surface coefficients (e.g., emission), these are often effective, averaged over various physical processes. Detailed knowledge on the surface kinetics may be obtained by sophisticated diagnostics, modeling, or a combination. These are often limited due to acquisition or computational requirements. Moreover, a comprehensive understanding of LTPs and related PSIs must be inherently multi-scale. This holds specifically for plasma modeling, where a consistent description requires sub-models on individual levels. In this work, the applicability of machine learning surrogate models to depict PSIs is discussed in the context of metallic thin film sputter deposition. Different surface models are assessed in terms of quality and abundance of data, as well as reliable physical descriptors. Lower physical fidelity data based on the transport and range of ions in matter simulations provide insight into the steady surface state; higher physical fidelity reactive molecular dynamics data capture also the dependence of a changing surface state. Both data sets are exploited for the training of corresponding machine learning models. The applied model architectures – based on artificial neural networks – are reviewed and the resulting prediction metrics are assessed. It is concluded that the obtained data-driven surrogate models entail the fidelity of the original physical models. They allow for a reliable and consistent multi-scale model coupling at significantly reduced computational costs. Envisioned applications of this modeling procedure include different plasma processes, materials, and phenomena (e.g., plasma catalysis). |
Wednesday, October 5, 2022 3:30PM - 3:45PM |
EW5.00004: Deep learning model for ion sputtering dynamics with molecular dynamics simulation Byungjo Kim, Jinkyu Bae, Hyunhak Jeong, Suyoung Yoo, Sang Ki Nam Atomistic simulations have emerged as an effective means for elucidating physical mechanisms of atomic-scale manufacturing. In particular, molecular dynamics (MD) simulations have been utilized for modeling plasma-surface interactions. This work presents a deep neural network based reduced order modeling combined with intensive MD simulations for revealing the fundamental nature of argon ion sputtering on copper substrate. Using MD simulations, sputtering yields, angular and energy distributions of both sputtered atoms and scattered ions are examined for varying characteristics of incident ions and the target surface. Then, beta variational autoencoder is used to reduce the high dimension of distributional outputs to a lower dimensional latent vector through an encoder part and reconstruct it to the original dimension through a decoder part. With the inputs and the corresponding reduced outputs, a regression model consisting of fully connected layers was trained in a supervised manner to approximate the nonlinear relations between the inputs and outputs. The present model enables us to handle the surface dynamics for sputtered and scattered species in an efficient and effective fashion, and can be further extended various other surface communications at the atomistic level. |
Wednesday, October 5, 2022 3:45PM - 4:00PM |
EW5.00005: Transfer Learning Model with Simulation and Experimental Data for Tool Virtualization in Poly-Si Etching Takeshi Nakayama, Tsutomu Tetsuka, Tomohiro Sekine, Takeshi Ohmori We investigate a model construction method for the digital twins (DTs) of semiconductor manufacturing tools (i.e., virtualized manufacturing tools) to maintain and correct manufacturing tools for nanoscale fabrication. Plasma etching is a non-linear phenomenon with a multi-input/output, and simulator calculations span extremely large spatiotemporal orders. Therefore, constructing a general-purpose plasma simulator and obtaining a DT using only the simulator is difficult. However, using machine learning to construct a DT model requires a large amount of time and experimental plasma etching data for training. Therefore, we utilize a transfer learning (TL) model that learns data from the simulation tools and experimental data obtained from the actual tools. We develop a DT model for plasma etching tools using the TL model and evaluate using a prediction task of the etching rate (ER) obtained from basic recipes. The TL model with the largest amount of simulation data showed the highest ER prediction accuracy. |
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