Modeling of the MEMS Reactive Ion Etching Process Using Neural Networks

Abstract Reactive ion etch (RIE) is commonly used in microelectromechanical systems (MEMS) fabrication as plasma etching method, where ions react with wafer surface substrate in plasma environment. Due to the importance of RIE in the MEMS field, two prediction models are established to predict the wafer status in reactive ion etching process: back-propagation neural network (BPNN) and principle component analysis BPNN (PCABPNN). These models have the potential to reduce the overall cost of ownership of MEMS equipment by increasing the wafer yield, and not depend upon monitoring wafers or expensive metrology rather it will enable inexpensive real-time wafer-to-wafer control applications in RIE. The artificial neural net (ANN) is trained with historical available input-output process data. Once trained, the ANN forecasts the process output rapidly if given the input values

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

매개분포근사를 통한 공정시스템 공학에서의 확률기계학습 접근법

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2021.8. 이종민.With the rapid development of measurement technology, higher quality and vast amounts of process data become available. Nevertheless, process data are ‘scarce’ in many cases as they are sampled only at certain operating conditions while the dimensionality of the system is large. Furthermore, the process data are inherently stochastic due to the internal characteristics of the system or the measurement noises. For this reason, uncertainty is inevitable in process systems, and estimating it becomes a crucial part of engineering tasks as the prediction errors can lead to misguided decisions and cause severe casualties or economic losses. A popular approach to this is applying probabilistic inference techniques that can model the uncertainty in terms of probability. However, most of the existing probabilistic inference techniques are based on recursive sampling, which makes it difficult to use them for industrial applications that require processing a high-dimensional and massive amount of data. To address such an issue, this thesis proposes probabilistic machine learning approaches based on parametric distribution approximation, which can model the uncertainty of the system and circumvent the computational complexity as well. The proposed approach is applied for three major process engineering tasks: process monitoring, system modeling, and process design. First, a process monitoring framework is proposed that utilizes a probabilistic classifier for fault classification. To enhance the accuracy of the classifier and reduce the computational cost for its training, a feature extraction method called probabilistic manifold learning is developed and applied to the process data ahead of the fault classification. We demonstrate that this manifold approximation process not only reduces the dimensionality of the data but also casts the data into a clustered structure, making the classifier have a low dependency on the type and dimension of the data. By exploiting this property, non-metric information (e.g., fault labels) of the data is effectively incorporated and the diagnosis performance is drastically improved. Second, a probabilistic modeling approach based on Bayesian neural networks is proposed. The parameters of deep neural networks are transformed into Gaussian distributions and trained using variational inference. The redundancy of the parameter is autonomously inferred during the model training, and insignificant parameters are eliminated a posteriori. Through a verification study, we demonstrate that the proposed approach can not only produce high-fidelity models that describe the stochastic behaviors of the system but also produce the optimal model structure. Finally, a novel process design framework is proposed based on reinforcement learning. Unlike the conventional optimization methods that recursively evaluate the objective function to find an optimal value, the proposed method approximates the objective function surface by parametric probabilistic distributions. This allows learning the continuous action policy without introducing any cumbersome discretization process. Moreover, the probabilistic policy gives means for effective control of the exploration and exploitation rates according to the certainty information. We demonstrate that the proposed framework can learn process design heuristics during the solution process and use them to solve similar design problems.계측기술의 발달로 양질의, 그리고 방대한 양의 공정 데이터의 취득이 가능해졌다. 그러나 많은 경우 시스템 차원의 크기에 비해서 일부 운전조건의 공정 데이터만이 취득되기 때문에, 공정 데이터는 ‘희소’하게 된다. 뿐만 아니라, 공정 데이터는 시스템 거동 자체와 더불어 계측에서 발생하는 노이즈로 인한 본질적인 확률적 거동을 보인다. 따라서 시스템의 예측모델은 예측 값에 대한 불확실성을 정량적으로 기술하는 것이 요구되며, 이를 통해 오진을 예방하고 잠재적 인명 피해와 경제적 손실을 방지할 수 있다. 이에 대한 보편적인 접근법은 확률추정기법을 사용하여 이러한 불확실성을 정량화 하는 것이나, 현존하는 추정기법들은 재귀적 샘플링에 의존하는 특성상 고차원이면서도 다량인 공정데이터에 적용하기 어렵다는 근본적인 한계를 가진다. 본 학위논문에서는 매개분포근사에 기반한 확률기계학습을 적용하여 시스템에 내재된 불확실성을 모델링하면서도 동시에 계산 효율적인 접근 방법을 제안하였다. 먼저, 공정의 모니터링에 있어 가우시안 혼합 모델 (Gaussian mixture model)을 분류자로 사용하는 확률적 결함 분류 프레임워크가 제안되었다. 이때 분류자의 학습에서의 계산 복잡도를 줄이기 위하여 데이터를 저차원으로 투영시키는데, 이를 위한 확률적 다양체 학습 (probabilistic manifold learn-ing) 방법이 제안되었다. 제안하는 방법은 데이터의 다양체 (manifold)를 근사하여 데이터 포인트 사이의 쌍별 우도 (pairwise likelihood)를 보존하는 투영법이 사용된다. 이를 통하여 데이터의 종류와 차원에 의존도가 낮은 진단 결과를 얻음과 동시에 데이터 레이블과 같은 비거리적 (non-metric) 정보를 효율적으로 사용하여 결함 진단 능력을 향상시킬 수 있음을 보였다. 둘째로, 베이지안 심층 신경망(Bayesian deep neural networks)을 사용한 공정의 확률적 모델링 방법론이 제시되었다. 신경망의 각 매개변수는 가우스 분포로 치환되며, 변분추론 (variational inference)을 통하여 계산 효율적인 훈련이 진행된다. 훈련이 끝난 후 파라미터의 유효성을 측정하여 불필요한 매개변수를 소거하는 사후 모델 압축 방법이 사용되었다. 반도체 공정에 대한 사례 연구는 제안하는 방법이 공정의 복잡한 거동을 효과적으로 모델링 할 뿐만 아니라 모델의 최적 구조를 도출할 수 있음을 보여준다. 마지막으로, 분포형 심층 신경망을 사용한 강화학습을 기반으로 한 확률적 공정 설계 프레임워크가 제안되었다. 최적치를 찾기 위해 재귀적으로 목적 함수 값을 평가하는 기존의 최적화 방법론과 달리, 목적 함수 곡면 (objective function surface)을 매개화 된 확률분포로 근사하는 접근법이 제시되었다. 이를 기반으로 이산화 (discretization)를 사용하지 않고 연속적 행동 정책을 학습하며, 확실성 (certainty)에 기반한 탐색 (exploration) 및 활용 (exploi-tation) 비율의 제어가 효율적으로 이루어진다. 사례 연구 결과는 공정의 설계에 대한 경험지식 (heuristic)을 학습하고 유사한 설계 문제의 해를 구하는 데 이용할 수 있음을 보여준다.Chapter 1 Introduction 1 1.1. Motivation 1 1.2. Outline of the thesis 5 Chapter 2 Backgrounds and preliminaries 9 2.1. Bayesian inference 9 2.2. Monte Carlo 10 2.3. Kullback-Leibler divergence 11 2.4. Variational inference 12 2.5. Riemannian manifold 13 2.6. Finite extended-pseudo-metric space 16 2.7. Reinforcement learning 16 2.8. Directed graph 19 Chapter 3 Process monitoring and fault classification with probabilistic manifold learning 20 3.1. Introduction 20 3.2. Methods 25 3.2.1. Uniform manifold approximation 27 3.2.2. Clusterization 28 3.2.3. Projection 31 3.2.4. Mapping of unknown data query 32 3.2.5. Inference 33 3.3. Verification study 38 3.3.1. Dataset description 38 3.3.2. Experimental setup 40 3.3.3. Process monitoring 43 3.3.4. Projection characteristics 47 3.3.5. Fault diagnosis 50 3.3.6. Computational Aspects 56 Chapter 4 Process system modeling with Bayesian neural networks 59 4.1. Introduction 59 4.2. Methods 63 4.2.1. Long Short-Term Memory (LSTM) 63 4.2.2. Bayesian LSTM (BLSTM) 66 4.3. Verification study 68 4.3.1. System description 68 4.3.2. Estimation of the plasma variables 71 4.3.3. Dataset description 72 4.3.4. Experimental setup 72 4.3.5. Weight regularization during training 78 4.3.6. Modeling complex behaviors of the system 80 4.3.7. Uncertainty quantification and model compression 85 Chapter 5 Process design based on reinforcement learning with distributional actor-critic networks 89 5.1. Introduction 89 5.2. Methods 93 5.2.1. Flowsheet hashing 93 5.2.2. Behavioral cloning 99 5.2.3. Neural Monte Carlo tree search (N-MCTS) 100 5.2.4. Distributional actor-critic networks (DACN) 105 5.2.5. Action masking 110 5.3. Verification study 110 5.3.1. System description 110 5.3.2. Experimental setup 111 5.3.3. Result and discussions 115 Chapter 6 Concluding remarks 120 6.1. Summary of the contributions 120 6.2. Future works 122 Appendix 125 A.1. Proof of Lemma 1 125 A.2. Performance indices for dimension reduction 127 A.3. Model equations for process units 130 Bibliography 132 초 록 149박

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

Carbon Nanotube Arrays for Intracellular Delivery and Biological Applications

Introducing nucleic acids into mammalian cells is a crucial step to elucidate biochemical pathways, modify gene expression in immortalized cells, primary cells, and stem cells, and intoduces new approaches for clinical diagnostics and therapeutics. Current gene transfer technologies, including lipofection, electroporation, and viral delivery, have enabled break-through advances in basic and translational science to enable derivation and programming of embryonic stem cells, advanced gene editing using CRISPR (Clustered regularly interspaced short palindromic repeats), and development of targeted anti-tumor therapy using chimeric antigen receptors in T-cells (CAR-T). Despite these successes, current transfection technologies are time consuming and limited by the inefficient introduction of test molecules into large populations of target cells, and the cytotoxicity of the techniques. Moreover, many cell types cannot be consistently transfected by lipofection or electroporation (stem cells, T-cells) and viral delivery has limitations to the size of experimental DNA that can be packaged. In this dissertation, a novel coverslip-like platform consisting of an array of aligned hollow carbon nanotubes (CNTs) embedded in a sacrificial template is developed that enhances gene transfer capabilities, including high efficiency, low toxicity, in an expanded range of target cells, with the potential to transfer mixed combinations of protein and nucleic acids. The CNT array devices are fabricated by a scalable template-based manufacturing method using commercially available membranes, eliminating the need for nano-assembly. High efficient transfection has been demonstrated by delivering various cargos (nanoparticles, dye and plasmid DNA) into populations of cells, achieving 85% efficiency of plasmid DNA delivery into immortalized cells. Moreover, the CNT-mediated transfection of stem cells shows 3 times higher efficiency compared to current lipofection methods. Evaluating the cell-CNT interaction elucidates the importance of the geometrical properties of CNT arrays (CNT exposed length and surface morphology) on transfection efficiency. The results indicate that densely-packed and shortly-exposed CNT arrays with planar surface will enhance gene delivery using this new platform. This technology offers a significant increase in efficiency and cell viability, along with the ease of use compared to current standard methods, which demonstrates its potential to accelerate the development of new cell models to study intractable diseases, decoding the signaling pathways, and drug discovery

High-Density 3D Pyramid-Shaped Microelectrode Arrays for Brain-Machine Interface Applications

RÉSUMÉ Les dispositifs médicaux dédiés aux enregistrements des activités neuronales et à la stimulation de tissus nerveux sont appelés interfaces cerveau-machines. Ils offrent un potentiel important pour restaurer diverses fonctions neurologiques perdues. Un élément clé dans la mise en œuvre des dispositifs est le réseau de microélectrodes (MEAs pour MicroElectrode Arrays en anglais) servant d’interface avec les tissus nerveux. Les MEA jouent un rôle important dans les implants lors d’expérimentations chroniques, ils doivent être fiables, stables et efficaces pour l'enregistrement et la stimulation à long terme. Les propriétés électrochimiques et la compatibilité biologique des microélectrodes sont des facteurs essentiels qui doivent être prises en compte lors de leur conception et fabrication. La présente thèse traite de la conception et la fabrication de MEA en silicium micro-usiné à haute densité et en forme de pyramides qui sont destinés à l’enregistrement et la stimulation intracorticals 3D. Nous nous concentrons principalement sur les techniques de microfabrication des électrodes et le développement de procédure du revêtement de matériaux nécessaires pour la biocompatibilité et protection des dispositifs implantables. Nous élaborons des microélectrodes à hauteur variable pour enregistrer des signaux neuronaux, sans perdre la capacité de microstimulation et tout en maintenant des impédances de faibles valeurs. Cette caractéristique est obtenue en modifiant la géométrie et la composition de matériaux utilisés, ce qui facilite l'injection de charge et la résolution spatiale élevée. Nous présentons une nouvelle technique de micro-usinage 3D à nombre réduit de masques comparé aux techniques existantes. Nous décrivons la mise en œuvre d’un MEA à haute densité (25 électrodes / 1,96 mm2) et à différentes longueurs d’électrodes. En outre, une nouvelle technique de masquage à base de film sec a été développée pour obtenir de très petites surfaces actives pour les microélectrodes qui sont à hauteur variable. Nous avons réduit les étapes du procédé de masquage de 14 à 6 par rapport à la méthode classique de masquage utilisé dans la littérature. Nous avons ensuite effectué, pour la première fois, une croissance directe sélective de nanotubes de carbone sur les têtes de microélectrodes de longueurs variables en utilisant la technique du dépôt chimique en phase vapeur assisté par plasma (Plasma-Enhanced Chemical Vapor Deposition - PECVD).----------ABSTRACT Neuroprosthetic devices that can record neural activities and stimulate the central nervous system (CNS), called brain-machine interfaces (BMI), offer significant potential to restore various lost neurologic functions. A key element in functions restoration is Microelectrode arrays (MEAs) implanted in neural tissues. MEAs, which act as an interface between bioelectronic devices and neural tissues, play an important role in chronic implants and must be reliable, stable, and efficient for long-term recording and stimulation. Electrochemical properties and biological compatibility of chronic microelectrodes are essential factors that must be taken into account in their design and fabrication. The present thesis deals with the design and fabrication of silicon micromachined, high-density, pyramid-shaped neural MEAs for intracortical 3D recording and stimulation. The focused is mainly on the MEAs fabrication techniques and development of coating materials process required with implantable devices with an ultimate purpose: elaborate variable-height microelectrodes to obtain consistent recording signals from small groups of neurons without losing microstimulation capabilities, while maintaining low-impedance pathways for charge injection, high charge transfer, and high-spatial resolution by altering the geometries and material compositions of the array. In the first part of the thesis, we present a new 3D micromachining technique with a single masking step in a time and cost effective manner. A high density 25 electrodes/ 1.96 mm2 MEA with varying lengths electrodes to access neurons that are located in different depths of cortical tissue was designed and fabricated. Furthermore, a novel dry-film based masking technique for procuring extremely small active area for variable-height electrodes has been developed. With this technology, we have reduced the masking process steps from 14 to 6 compared to the conventional masking method. We have then reported for the first time a selective direct growth of carbon nanotubes (CNTs) on the tips of 3D MEAs using Plasma Enhanced Chemical Vapor Deposition (PECVD) that could enhance electrical properties of the electrodes significantly. The CNT coating led to a 5-fold decrease in impedance and a 600-fold increase in charge transfer compared with Pt electrode. Finally, we have highlighted the importance of the coating MEAs with bioactive molecules (Poly-D-lysine) and polyethylene glycol (PEG) hydrogels to minimize the immune response of the neural tissue to implanted MEAs by in vitro cell-culture tests

Development Of Carbon Based Neural Interface For Neural Stimulation/recording And Neurotransmitter Detection

Electrical stimulation and recording of neural cells have been widely used in basic neuroscience studies, neural prostheses, and clinical therapies. Stable neural interfaces that effectively communicate with the nervous system via electrodes are of great significance. Recently, flexible neural interfaces that combine carbon nanotubes (CNTs) and soft polymer substrates have generated tremendous interests. CNT based microelectrode arrays (MEAs) have shown enhanced electrochemical properties compared to commonly used electrode materials such as tungsten, platinum or titanium nitride. On the other hand, the soft polymer substrate can overcome the mechanical mismatch between the traditional rigid electrodes (or silicon shank) and the soft tissues for chronic use. However, most fabrication techniques suffer from low CNT yield, bad adhesion, and limited controllability. In addition, the electrodes were covered by randomly distributed CNTs in most cases. In this study, a novel fabrication method combining XeF2 etching and parylene deposition was presented to integrate the high quality vertical CNTs grown at high temperature with the heat sensitive parylene substrate in a highly controllable manner. Lower stimulation threshold voltage and higher signal to noise ratio have been demonstrated using vertical CNTs bundles compared to a Pt electrode and other randomly distributed CNT films. Adhesion has also been greatly improved. The work has also been extended to develop cuff shaped electrode for peripheral nerve stimulation. Fast scan cyclic voltammetry is an electrochemical detection technique suitable for in-vivo neurotransmitter detection because of the miniaturization, fast time response, good sensitivity and selectivity. Traditional single carbon fiber microelectrode has been limited to single detection for in-vivo application. Alternatively, pyrolyzed photoresist film (PPF) is a good candidate for this application as they are readily compatible with the microfabrication process for precise fabrication of microelectrode arrays. By the oxygen plasma treatment of photoresist prior to pyrolysis, we obtained carbon fiber arrays. Good sensitivity in dopamine detection by this carbon fiber arrays and improved adhesion have been demonstrated

The 2022 Plasma Roadmap: low temperature plasma science and technology

The 2022 Roadmap is the next update in the series of Plasma Roadmaps published by Journal of Physics D with the intent to identify important outstanding challenges in the field of low-temperature plasma (LTP) physics and technology. The format of the Roadmap is the same as the previous Roadmaps representing the visions of 41 leading experts representing 21 countries and five continents in the various sub-fields of LTP science and technology. In recognition of the evolution in the field, several new topics have been introduced or given more prominence. These new topics and emphasis highlight increased interests in plasma-enabled additive manufacturing, soft materials, electrification of chemical conversions, plasma propulsion, extreme plasma regimes, plasmas in hypersonics, data-driven plasma science and technology and the contribution of LTP to combat COVID-19. In the last few decades, LTP science and technology has made a tremendously positive impact on our society. It is our hope that this roadmap will help continue this excellent track record over the next 5–10 years.Peer ReviewedPostprint (published version

The 2022 Plasma Roadmap: low temperature plasma science and technology

Documento escrito por un elevado número de autores/as, solo se referencia el/la que aparece en primer lugar y los/as autores/as pertenecientes a la UC3M.The 2022 Roadmap is the next update in the series of Plasma Roadmaps published by Journal of Physics D with the intent to identify important outstanding challenges in the field of low-temperature plasma (LTP) physics and technology. The format of the Roadmap is the same as the previous Roadmaps representing the visions of 41 leading experts representing 21 countries and five continents in the various sub-fields of LTP science and technology. In recognition of the evolution in the field, several new topics have been introduced or given more prominence. These new topics and emphasis highlight increased interests in plasma-enabled additive manufacturing, soft materials, electrification of chemical conversions, plasma propulsion, extreme plasma regimes, plasmas in hypersonics and data-driven plasma science.Cristina Canal acknowledges PID2019-103892RB-I00/AEI/10.13039/501100011033 Project (AEI) and the Generalitat de Catalunya for the ICREA Academia Award and SGR2017-1165. The research by Annemie Bogaerts was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Synergy Grant 810182 SCOPE). Eduardo Ahedo was funded by Spain's Agencia Estatal de Investigación, under Grant No. PID2019-108034RB-I00 (ESPEOS Project)