Real-time Plasma Research Articles

First application of a digital mirror Langmuir probe for real-time plasma diagnosis.

For the first time, a digital Mirror Langmuir Probe (MLP) has successfully sampled plasma temperature, ion saturation current, and floating potential together on a single probe tip in real time in a radio-frequency driven helicon linear plasma device. This is accomplished by feedback control of the bias sweep to ensure a good fit to I-V characteristics with a high frequency, high power digital amplifier, and field-programmable gate array controller. Measurements taken by the MLP were validated by a low speed I-V characteristic manually collected during static plasma conditions. Plasma fluctuations, induced by varying the axial magnetic field (f̃ = 10Hz), were also successfully monitored with the MLP. Further refinement of the digital MLP pushes it toward a turn-key system that minimizes the time to deployment and lessens the learning curve, positioning the digital MLP as a capable diagnostic for the study of low radio-frequency plasma physics. These demonstrations bolster confidence in fielding such digital MLP diagnostics in magnetic confinement experiments with high spatial and adequate temporal resolution, such as edge plasma, scrape-off layer, and divertor probes.

Preliminary design of real-time plasma control system for CFETR

The China Fusion Engineering Test Reactor (CFETR), aiming at demonstrating fusion energy production in self-sufficient burning in different advanced operation scenarios, has entered the stage of engineering design and R&D (research and development). The real-time plasma control system (PCS), as a critical system controlling the evolution of plasma parameters that are necessary to operate CFETR, is designed synchronously. The CFETR PCS is composed of system architecture, integrated control algorithms, and two assistant tools PCS-SDP (Software Development Platform) and PCS-VP (Verification Platform) for algorithm development and verification. Taking the steady-state operation and machine safety into account, the requirement and conceptual design of the system architecture of CFETR PCS is introduced in this paper. PCS-SDP, a visual algorithm development platform, has completed the preliminary design and a prototype has been built to validate the design and technical feasibility with the control algorithm development for EAST. PCS-VP is under preliminary design taking ITER PCSSP (Plasma Simulation Platform) design as reference. These two tool platforms will strongly support and accelerate the development of CFETR PCS.

Deep sequence to sequence learning-based prediction of major disruptions in ADITYA tokamak

Major disruptions in tokamak plasmas need to be identified well before their occurrence and appropriately mitigated. Otherwise, it may dump the heat and electromagnetic load to the vessel and its surrounding plasma-facing components. A predictor system based on precursor diagnostics may help in forecasting the disruptive events in tokamak plasma and raise the alert beforehand to take necessary actions to prevent the major damages inside the vacuum vessel. This paper describes a predictor system built with a few selected diagnostic signals from the ADITYA tokamak and trained on a time-sequence long short-term memory network to predict the occurrence of disruption to 7–20 ms in advance with an accuracy of 89% on the testing set of 36 disruptive and 6 non-disruptive shots. This real-time network can infer to one time-step results under 170 µs on an Intel Xeon processor running python, suggesting minimal computation cost and best suited for the real-time plasma control applications.

Proof of concept of a fast surrogate model of the VMEC code via neural networks in Wendelstein 7-X scenarios

In magnetic confinement fusion research, the achievement of high plasma pressure is key to reaching the goal of net energy production. The magnetohydrodynamic (MHD) model is used to self-consistently calculate the effects the plasma pressure induces on the magnetic field used to confine the plasma. Such MHD calculations—usually done computationally—serve as input for the assessment of a number of important physics questions. The variational moments equilibrium code (VMEC) is the most widely used to evaluate 3D ideal-MHD equilibria, as prominently present in stellarators. However, considering the computational cost, it is rarely used in large-scale or online applications (e.g. Bayesian scientific modeling, real-time plasma control). Access to fast MHD equilibria is a challenging problem in fusion research, one which machine learning could effectively address. In this paper, we present artificial neural network (NN) models able to quickly compute the equilibrium magnetic field of Wendelstein 7-X. Magnetic configurations that extensively cover the device operational space, and plasma profiles with volume-averaged normalized plasma pressure ⟨β⟩ (β = ) up to 5% and non-zero net toroidal current are included in the data set. By using convolutional layers, the spectral representation of the magnetic flux surfaces can be efficiently computed with a single network. To discover better models, a Bayesian hyper-parameter search is carried out, and 3D convolutional NNs are found to outperform feed-forward fully-connected NNs. The achieved normalized root-mean-squared error, the ratio between the regression error and the spread of the data, ranges from 1% to 20% across the different scenarios. The model inference time for a single equilibrium is on the order of milliseconds. Finally, this work shows the feasibility of a fast NN drop-in surrogate model for VMEC, and it opens up new operational scenarios where target applications could make use of magnetic equilibria at unprecedented scales.

IOT-based in situ condition monitoring of semiconductor fabrication equipment for e-maintenance

PurposeThe purpose of this paper is to demonstrate industrial Internet of Things (IIoT) solution to improve the equipment condition monitoring with equipment status data and process condition monitoring with plasma optical emission spectroscopy data, simultaneously. The suggested research contributes e-maintenance capability by remote monitoring in real time.Design/methodology/approachSemiconductor processing equipment consists of more than a thousand of components, and unreliable condition of equipment parts leads to the failure of wafer production. This study presents a web-based remote monitoring system for physical vapor deposition (PVD) systems using programmable logic controller (PLC) and Modbus protocol. A method of obtaining electron temperature and electron density in plasma through optical emission spectroscopy (OES) is proposed to monitor the plasma process. Through this system, parts that affect equipment and processes can be controlled and properly managed. It is certainly beneficial to improve the manufacturing yield by reducing errors from equipment parts.FindingsA web-based remote monitoring system provides much of benefits to equipment engineers to provide equipment data for the equipment maintenance even though they are physically away from the equipment side. The usefulness of IIoT for the e-maintenance in semiconductor manufacturing domain with the in situ monitoring of plasma parameters is convinced. The authors found the average electron temperature gradually with the increase of Ar carrier gas flow due to the increased atomic collisions in PVD process. The large amount of carrier gas flow, in this experimental case, was 90 sccm, dramatically decreasing the electron temperature, which represents kinetic energy of electrons.Research limitations/implicationsSemiconductor industries require high level of data security for the protection of their intellectual properties, and it also falls into equipment operational condition; however, data security through the Internet communication is not considered in this research, but it is already existing technology to be easily adopted by add-on feature.Practical implicationsThe findings indicate that crucial equipment parameters are the amount of carrier gas flow rate and chamber pressure among the many equipment parameters, and they also affect plasma parameters of electron temperature and electron density, which directly affect the quality of metal deposition process result on wafer. Increasing the gas flow rate beyond a certain limit can yield the electron temperature loss to have undesired process result.Originality/valueSeveral research studies on data mining with semiconductor equipment data have been suggested in semiconductor data mining domain, but the actual demonstration of the data acquisition system with real-time plasma monitoring data has not been reported. The suggested research is also valuable in terms of high cost and complicated equipment manufacturing.

Plasma current profile reconstruction for EAST based on Bayesian inference

Determining the distribution of plasma current in the equilibrium state is one of the most important steps to realize effective and safe operation of tokamak. In this study, a novel reconstruction code based on Bayesian inference is developed to infer the plasma current distribution for experiment analysis of EAST. Without iteratively solving Grad-Shafranov (G-S) equation to find an optimal fit for the external magnetic diagnostic measurements, the distribution of toroidal current density and poloidal flux can be rapidly derived in a probabilistic manner. The reconstructed results are consistent with the results based on equilibrium fitting (EFIT) code, and the execution time is less than 1 ms for each time slice, which indicates its potential for application in future real-time plasma feedback control.

The Implementation of Real-Time Plasma Electron Density Calculation Based on FPGA for Tokamak Devices

The Implementation of Real-Time Plasma Electron Density Calculation Based on FPGA for Tokamak Devices

Conceptual design of the COMPASS-U control systems

The COMPASS-U tokamak is an advanced and more complicated successor of the COMPASS tokamak. Almost all main technological parts will be different. Both the higher performance of COMPASS-U and the increased number of parts and systems lead to a higher risk of failure. Therefore, new control systems must be developed regarding handling dangerous situations and integrating new systems. The COMPASS-U also opens the opportunity to make significant changes in the current systems based on the experience gained from COMPASS operation and to upgrade the systems to nowadays technologies.This article focuses on the conceptual design of the COMPASS-U control systems. It shows all CODAC (Control, Data Access, and Communication) systems their purpose, functionality, operation principle, interaction, and main used technologies.The COMPASS-U will use EPICS (Experimental Physics and Industrial Control System) for Slow control and supervision system. The real-time plasma control system will utilize MARTe2 (Multi-threaded Application Real-Time executor). The base control loop period of the Real-time plasma control system will be 50 μs as of COMPASS.

Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. A potential application of the model to real-time profile estimation is demonstrated.

Assessment of measurement performance for a low field side IDTT plasma position reflectometry system

The Italian Divertor Test Tokamak (IDTT) facility will study advanced exhaust solutions applicable to DEMO. This new machine also opens the possibility to test and validate relevant non-magnetic control diagnostics in support of a DEMO design implementation. Reflectometry, compatible with a full grade reactor implementation, has been proposed as a source of real-time (RT) plasma position and shape measurements for control purposes, in replacement or complement of standard magnetic measurements, to ensure reliability and safety on the machine. Additionally, such a system can be useful to obtain the characteristics of edge turbulence, useful for Neoclassical Tearing Mode (NTM) control or to improve the efficiency of other diagnostics as Collective Thomson Scattering (CTS). This new control technique based on multiple, poloidally distributed, non-magnetic measurements, must be tested, in all of its aspects, before it can be fully implemented in future fusion reactors. IDTT will be one of the best candidates to implement and build a knowledge database of nonstandard reflectometry (away from the equatorial plane) that will be needed on DEMO and is currently unavailable. The performance of three Ordinary mode (O-mode) Plasma Position Reflectometers (PPR) at the Lower Field Side (LFS) on IDTT is assessed using the two-dimensional (2D) full-wave Finite-Difference Time-Domain (FDTD) code, REFMULF, in two of the foreseen IDTT plasma scenarios: 5MA Single Null (SN) and Double Null (DN) configurations.

The integrated diagnostic suite of the C-2W experimental field-reversed configuration device and its applications.

In the current experimental device of TAE Technologies, C-2W (also called "Norman"), record breaking advanced beam-driven field-reversed configuration (FRC) plasmas are produced and sustained in steady state utilizing variable energy neutral beams (15-40 keV, total power up to 20MW), advanced divertors, bias electrodes, and an active plasma control system. This fully operational experiment is coupled with a fully operational suite of advanced diagnostic systems. The suite consists of 60+ individual systems spanning 20 categories, including magnetic sensors, Thomson scattering, interferometry/polarimetry, spectroscopy, fast imaging, bolometry, reflectometry, charged and neutral particle analysis, fusion product detection, and electric probes. Recently, measurements of main ion temperatures via a diagnostic neutral beam, axial profiles of energy flux from an array of bolometers, and divertor and edge plasma parameters via an extensive set of electric probes, interferometers, and spectrometers have all been made available. All the diagnostics work together to provide a complete picture of the FRC, fast-ion inventory, and edge plasma details enabling tomographic reconstruction of plasma parameter profiles and real-time plasma control.

Investigating Complex Samples with Molograms of Low-Affinity Binders.

In vitro diagnostics relies on the quantification of minute amounts of a specific biomolecule, called biomarker, from a biological sample. The majority of clinically relevant biomarkers for conditions beyond infectious diseases are detected by means of binding assays, where target biomarkers bind to a solid phase and are detected by biochemical or physical means. Nonspecifically bound biomolecules, the main source of variation in such assays, need to be washed away in a laborious process, restricting the development of widespread point-of-care diagnostics. Here, we show that a diffractometric assay provides a new, label-free possibility to investigate complex samples, such as blood plasma. A coherently arranged sub-micron pattern, that is, a peptide mologram, is created to demonstrate the insensitivity of this diffractometric assay to the unwanted masking effect of nonspecific interactions. In addition, using an array of low-affinity binders, we also demonstrate the feasibility of molecular profiling of blood plasma in real time and show that individual patients can be differentiated based on the binding kinetics of circulating proteins.

The upgrade of the control and data acquisition system of RFXMod2

RFX-mod2 is an upgrade of RFX-mod that will use a modified shell and mechanical structure in order to enhance plasma-shell proximity and therefore to improve plasma control. The Control and Data Acquisition System for most of the plant systems and diagnostics previously used in RFX-mod will be refurbished, while others will be completely re-built. The most important component that will be completely renewed is the ElectroMagnetic probe (EM) data acquisition system, where a new architecture based on XILINX Zynq FPGA will be used to carry out at the same time both high-speed data acquisition and resampled data streaming for active plasma control. The use of MDSplus will be retained in RFX-mod2, while the MARTe framework used for real-time plasma control will be replaced by MARTe2, a new framework developed under strict software quality standards. Plant control in RFX-mod2 will be supervised by WinCC-OA, replacing the previous FactoryLink SCADA systems. Older plant systems such as vacuum control based on outdated S5 PLCs will be updated and will use OPC-UA for communication with the supervisory control system.

Real-time feedback control of the impurity emission front in tokamak divertor plasmas

In magnetic confinement thermonuclear fusion the exhaust of heat and particles from the core remains a major challenge. Heat and particles leaving the core are transported via open magnetic field lines to a region of the reactor wall, called the divertor. Unabated, the heat and particle fluxes may become intolerable and damage the divertor. Controlled ‘plasma detachment’, a regime characterized by both a large reduction in plasma pressure and temperature at the divertor target, is required to reduce fluxes onto the divertor. Here we report a systematic approach towards achieving this critical need through feedback control of impurity emission front locations and its experimental demonstration. Our approach comprises a combination of real-time plasma diagnostic utilization, dynamic characterization of the plasma in proximity to the divertor, and efficient, reliable offline feedback controller design.

Perspectives on Machine Learning-Assisted Plasma Medicine: Toward Automated Plasma Treatment

Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications. Predictive modeling of the mutual interactions between the plasma and biological surfaces and, thus, systematic approaches to quantify and predict plasma treatment outcomes remain largely elusive due to the lack of mechanistic understanding of plasma–surface interactions that can span across vastly different length scales and timescales. In addition, real-time sensing capabilities in biomedical CAP devices are often limited, which can be detrimental to plasma treatment due to the intrinsic plasma and surface variability during the treatment, as well as sensitivity to external perturbations. All of these challenges can make reproducible and effective plasma treatment of biological surfaces difficult to realize, which is further compounded by errors due to the human operation of hand-held CAP devices. Machine learning and data-driven approaches can be particularly useful in addressing these challenges in three major ways: 1) data-driven modeling of hard-to-model plasma–surface interactions and plasma treatment outcomes; 2) learning data analytics for plasma and surface diagnostics in real time; and 3) developing predictive controllers that enable reliable and effective CAP treatments. This article discusses the promise of machine learning to accelerate plasma medicine research in these areas, toward machine learning-assisted and automated CAP treatment of complex biological surfaces.

Real-time feedback control system for ADITYA-U horizontal plasma position stabilisation

The ADITYA-U tokamak (R0 = 0.75 m, a = 0.25 m) is designed to shape plasma column in both single and double null diverter configurations. It is quite well known that sustaining a shaped plasma in tokamak requires very good plasma column position control, both horizontal and vertical. An FPGA-based proportional–integral–derivative (PID) control system has been designed and operated to achieve horizontal plasma position control in ADITYA-U tokamak. The complete system has been rigorously tested with sample signals before implementing to the ADITYA-U plasma discharges. The control system is integrated and time-synchronized with the plasma discharge operation of ADITYA –U. Furthermore, the system has been trained to take appropriate actions during the disruption or plasma failure in the tokamak operation. Detailed experimental results have been obtained by the operation of the digital PID controller. The complete design, installation, operation, tuning of the system along with all the relevant testing and operating experience of the digital PID controller for real-time horizontal plasma position control is presented in the paper.

Distribution of Apolipoprotein A1 Polymorphism (G-75A and C+83T) in Patients with Diabetic Foot Ulcers-A Parallel Group Hospital Based Observational Study

Introduction: Diabetic Foot Ulcer (DFU), a serious complications of diabetes mellitus is a result of persistent low grade infection. The Apolipoprotein A1 (ApoA1) has an anti-inflammatory role and therefore can influence the chronic inflammation associated with the DFU. Polymorphisms of ApoA1gene have been implicated as determinants of plasma HDL-C and Apo A1 levels. However, the influence of ApoA1 polymorphism on susceptibility to DFU has not been studied. Aim: To study the distribution of ApoA1 polymorphism (G-75A and C+83T) and association between the polymorphism and the risk of DFU in patients with Type 2 Diabetes Mellitus (T2DM) so that timely detection and prevention of DFU can be done. Materials and Methods: This was a hospital based observational study on patients of DFU (n=80), diabetes mellitus without ulcers (n=80) and normal controls (n=75). ApoA1 polymorphism (G-75A and C+83T) was detected by Real Time Polymerase Chain Reaction (RTPCR) technique and plasma ApoA1 by immunoturbidimetric assay using blood collected in EDTA. Data was analysed using IBM® Statistical Package for Social Sciences (SPSS) 21.0 software. A p<0.05 was considered as statistically significant. Results: The GA and CC were the most predominant genotype in all the groups. HDL and ApoA1 were significantly lower in GG (p=0.009, p=0.03) and CT (p=0.03, p=0.002) compared to GA and CC. The APOA1-75A allele and +83C allele were associated with raised levels of HDL and ApoA1 in T2DM and DFU (p<0.05). Conclusion: The two polymorphism G-75A and C+83T were found to be equally distributed across the study populations. These polymorphisms were associated with serum levels of ApoA1 and HDL in the DFU patients.

CAKE: Consistent Automatic Kinetic Equilibrium reconstruction

• The Consistent Automatic Kinetic Equilibrium reconstruction tool (CAKE) has been developed and implemented at DIII-D. • Data filtering fitting procedures and well as parameter selection enable automatic kinetic equilibrium fitting using EFIT. • Procedure can robustly reach low numerical error equilibria needed for MHD stability calculations. • Provides inputs of consistent settings for database statistical analysis or for machine learning training sets. Magnetohydrodynamic equilibrium reconstructions with kinetic constraints are an essential input of many tokamak stability and transport analysis workflows. The reconstructions themselves also contain useful information about pressure and current density distributions within the plasma which can be used to drive other calculations. While standardized magnetic-only, and partially kinetic equilibrium reconstruction is widely available and used, reconstructions for well diagnosed machines such as DIII-D, that incorporates a large and heterogeneous set of internal diagnostic measurements yet robustly reaching high numerical accuracy have been a time intensive, manual process. The Consistent Automatic Kinetic Equilibrium reconstruction tool (CAKE) has been developed and implemented at DIII-D for producing low error, kinetically constrained reconstructions without human intervention, and to identify strategies for doing so that would be broadly applicable. The techniques employed to allow CAKE to handle data of limited quality and availability, as well as the full automation of the workflow may assist future efforts to form kinetic constraint profiles in real time plasma control applications.

Association of plasma fatty acids pattern with omentin gene expression in human adipose tissues: A cross-sectional study

Background and aimsOmentin, as an adipokine, has been reported to improve insulin resistance and inflammation may be related to fatty acids (FAs). Plasma FAs can be used as biomarkers of dietary FAs and endogenous FA exposure. We aimed to evaluate the association between plasma FAs pattern and omentin gene expression in adipose tissue (AT). Methods and resultsVisceral and subcutaneous AT and fasting blood were gathered from 97 adults aged >18 years. Participants were already admitted to hospitals for elective abdominal surgery. Dietary intakes were assessed using a food frequency questionnaire. The relative omentin gene expression in visceral and subcutaneous AT was measured by Real-Time PCR and plasma FAs was determined by gas chromatography. The principal component analysis was performed to derive the FAs pattern from plasma individual FAs. Three patterns were derived from plasma FAs, 1) high de-novo lipogenesis (DNL), 2) high trans saturated fatty acids (SFA), and docosahexaenoic acid (trans-SFA/DHA), and 3) high long-chain SFA (LC-SFA). After adjustment for age, sex, and insulin concentration, only the LC-SFA pattern was associated with omentin gene expression in visceral AT (β = 2.25, P = 0.03). Other patterns were not associated with omentin gene expression in visceral and subcutaneous AT. ConclusionA pattern characterized by high levels of myristic acid (14:0), heptadecanoic acid (17:0), pentadecanoic acid (15:0), and Cis_heptadecanoic acid (17:1), which named LC-SFA was related to omentin gene expression in visceral AT.

Jitter-free 40-fs 375-keV electron pulses directly accelerated by an intense laser beam and their application to direct observation of laser pulse propagation in a vacuum

We report the generation of ultrashort bright electron pulses directly driven by irradiating a solid target with intense femtosecond laser pulses. The duration of electron pulses after compression by a phase rotator composed of permanent magnets was measured as 89 fs via the ponderomotive scattering of electron and laser pulses, which were almost at the compression limit due to the dispersion of the electron optics. The electron pulse compression system consisting of permanent magnets enabled extremely high timing stability between the laser pulse and electron pulse. The long-term RMS arrival time drift was below 14 fs in 4 h, which was limited by the resolution of the current setup. Because there was no time-varying field to generate jitter, the timing jitter was essentially reduced to zero. To demonstrate the capability of the ultrafast electron pulses, we used them to directly visualize laser pulse propagation in a vacuum and perform 2D mapping of the electric fields generated by low-density plasma in real time.