Predicting the etch and deposition profiles created using plasma processes is challenging due to the complexity of plasma discharges and plasma-surface interactions. Volume-averaged global models allow for efficient prediction of important processing parameters and provide a means to quickly determine the effect of a variety of process inputs on the plasma discharge. However, global models are limited based on simplifying assumptions to describe the chemical reaction network. Here a database of 128 reactions is compiled and their corresponding rate constants collected from 24 sources for an Ar/CF4 plasma using the platform RODEo (Recipe Optimization for Deposition and Etching). Six different reaction sets were tested which employed anywhere from 12 to all 128 reactions to evaluate the impact of the reaction database on particle species densities and electron temperature. Because many the reactions used in our database had conflicting rate constants as reported in literature, we also present a method to deal with those uncertainties when constructing the model which includes weighting each reaction rate and filtering outliers. By analyzing the link between a reaction’s rate constant and its impact on the predicted plasma densities and electron temperatures, we determine the conditions at which a reaction is deemed necessary to the plasma model. The results of this study provide a foundation for determining which minimal set of reactions must be included in the reaction set of the plasma model.
more »
« less
Exploring data-driven models for spatiotemporally local classification of Alfvén eigenmodes
Abstract Alfvén eigenmodes (AEs) are an important and complex class of plasma dynamics commonly observed in tokamaks and other plasma devices. In this work, we manually labeled a small database of 26 discharges from the DIII-D tokamak in order to train simple neural-network-based models for classifying AEs. The models provide spatiotemporally local identification of four types of AEs by using an array of 40 electron cyclotron emission (ECE) signals as inputs. Despite the minimal dataset, this strategy performs well at spatiotemporally localized classification of AEs, indicating future opportunities for more sophisticated models and incorporation into real-time control strategies. The trained model is then used to generate spatiotemporally-resolved labels for each of the 40 ECE measurements on a much larger database of 1112 DIII-D discharges. This large set of precision labels can be used in future studies for advanced deep predictors and new physical insights.
more »
« less
- Award ID(s):
- 2329765
- PAR ID:
- 10445615
- Date Published:
- Journal Name:
- Nuclear Fusion
- Volume:
- 62
- Issue:
- 10
- ISSN:
- 0029-5515
- Page Range / eLocation ID:
- 106014
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. Despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.more » « less
-
Guharay, S; Wada, M (Ed.)At or near atmospheric pressure, overvolted gas breakdown results in a streamer formation. In many applications of non-thermal plasma where efficient excited species generation is critical, the streamers are quenched to prevent it from reaching the arc phase. This can be achieved by repetitive nano second pulsing or dielectric barrier discharges were the dielectric charging quenches the arc formation. In such discharges, the plasma characteristics such as electron and ion densities and the production of excited species is determined by the streamer properties. Over the past five decades, a vast amount of experimental and computational work has been accumulated to establish a well-accepted theory of streamer formation and propagation. In this article we discuss the fluid models for streamers and quantify some macroscopic properties which can inform specific applications. We discuss in detail the fluid equations needed to model streamers and several schemes of parametrization of the transport and electron collisional processes. From an application point of view, the steamer simulations are used to quantify the excited species production by electron impact. This information is used to predict the specific outcomes via the plasma chemical conversion pathways. We present results of streamer discharges for three applications which are of technological importance to illustrate this approach: Plasma-assisted combustion, remediation of toxic gases, and plasma medicine. For plasma-assisted combustion the results of hydrogen ignition are discussed since non-hydrocarbon-based fuels such as hydrogen and ammonia are potential fuel candidates to reduce greenhouse gases. For the remediation of toxic gases, we discuss the removal of SOX/NOx from flue gas. Plasma medicine is a relatively new field and repetitive nano-second pulsed discharges in a helium gas carrier shows promise as a reactive plasma source for treating biological material. We discuss the helium metastable production in a streamer discharge since this species leads to the production of OH radicals which plays an important role.more » « less
-
This paper investigates the self-pulsing of dielectric barrier discharges (DBDs) at low driving frequencies. In particular, (a) the dependence of current on the product pd of gas pressure p and the gas gap length d, (b) the effects of lossy dielectrics (in resistive discharges) and large dielectric permittivity (in ferroelectrics) on current dynamics, (c) the transition from Townsend to a dynamic capacitively coupled plasma (CCP) discharge with changing pd values, and (d) the transition from Townsend to a high-frequency CCP regime with increasing the driving frequency. A one-dimensional fluid model of argon plasma is coupled to an equivalent RC circuit for lossy dielectrics. Our results show multiple current pulses per AC period in Townsend and CCP discharge modes which are explained by uncoupled electron–ion transport in the absence of quasineutrality and surface charge deposition at dielectric interfaces. The number of current pulses decreases with an increasing applied frequency when the Townsend discharge transforms into the CCP discharge. The resistive barrier discharge with lossy dielectrics exhibits Townsend and glow modes for the same pd value (7.6 Torr cm) for higher and lower resistances, respectively. Finally, we show that ferroelectric materials can amplify discharge current in DBDs. Similarities between current pulsing in DBD, Trichel pulses in corona discharges, and subnormal oscillations in DC discharges are discussed.more » « less
-
This paper systematically evaluates how, and to what extent, nanosecond repetitively pulsed (NRP) discharges modify the laminar flame speed of methane–air mixtures at ambient conditions, using both experiments in a narrow-channel quartz burner and a one-dimensional plasma-combustion model described in an accompanying paper (Part I). By varying the discharge location relative to the flame, four actuation strategies were explored at variable pulse repetition frequency: (i) discharges far ahead of the flame, (ii) pre-treatment of fresh reactants, (iii) direct (insitu) plasma–flame overlap, and (iv) a combination of pre-treatment and insitu interaction. Results show that acoustic waves produced by upstream discharges can reduce flame speed by as much as 30%–40%; while partially overlapping the discharge with the flame significantly accelerates it, with measured enhancements of up to 50% in both model and experiment. Flame speed modification by plasma increased with pulse repetition frequency, so that the envelope of performance enhancement reported here is limited by the highest frequencies tested (8kHz). The model captures these trends by attributing the detrimental effects to pressure-wave disturbances and the beneficial effects to radical-seeding and mild heat addition in, and close to, the reaction zone. These observations may help shed light on previously reported experiments and are here presented in a unified manner by focusing on a fundamental combustion metric (laminar flame speed), to give generality to the results obtained in laminar flames. The results demonstrate how spatio-temporal positioning of the discharge governs whether plasma aids or hinders the flame, ultimately guiding the design of optimal plasma-assisted combustion strategies.more » « less
An official website of the United States government

