Critical Process Steps Research Articles

Deep Learning-Based Branch-Cut Method for InSAR Two-Dimensional Phase Unwrapping

Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the traditional 2-D PU is an ill-posed inverse problem, which means that regardless of how skillful the PU algorithm designer is, it is impossible to design an algorithm that can correctly process all the 2-D PU situations, i.e., we can only design the best PU algorithm in the statistical sense. Therefore, accumulating PU processing experience from different study cases is important for PU algorithm design. Currently, the deep learning (DL) technique provides a potential framework to accumulate processing experience, and a flood of valuable data coming from different InSAR sensors provides the ability to enable the learning-based PU technique outside the traditional model-based technique. In this article, we transform the 2-D PU problem into a learnable image semantic segmentation problem and propose a DL-based branch-cut deployment method (abbreviated as BCNet). To start, we propose the optimal branch-cut connection criterion (referred to as OPT-BC) with the reference unwrapped phase given. Next, using the relationship between the residue and branch-cut as the learning objective, BCNet is trained using the samples provided by OPT-BC to produce the branch-cut result. Finally, the traditional branch-cut method is utilized to perform the postprocessing procedure to obtain the final PU result. The experimental results demonstrate that the proposed BCNet-based PU method is a near-real-time 2-D PU algorithm, and its accuracy outperforms the traditional model- and learning-based 2-D PU methods.

Optimized Cell Mixing Facilitates the Reproducible Bioprinting of Constructs with High Cell Viability

Bioprinting with cell-laden hydrogels (bioink) requires the careful mixing of cells with the hydrogel carrier to ensure that the bioink is homogeneous and functional, and the printing results are reproducible. Bioink preparation is therefore a critical process step that must accommodate the specific rheological properties of different bioinks. Here, we developed a reproducible method for the optimized mixing of cells and hydrogel carriers that can be integrated into current bioprinting processes. First, we tested and optimized different mixing devices for their effect on bioink homogeneity and rheological properties, resulting in a low-shear process for the preparation of homogenous bioinks. Based on these findings, we evaluated the impact of different cell densities on the rheological profile of bioinks according to shear and temperature, and estimated the impact of shear stress intensity and duration on 1.1B4 cells. Finally, we integrated the optimized mixing method into a current printing process and monitored the printed construct for 14 days to confirm cell viability. We found that the cell viability in the printed cell-laden constructs remained in excess of 91% after 14 days.

Monitoring of critical processing steps during the production of high dense 3D alumina parts using Fused Filament Fabrication technology

Among methods covered under the additive manufacturing family, the Fused Filament Fabrication (FFF) is considered one of the most accessible, variable, and cost-effective prototyping techniques for processing polymer and composite materials. In this paper, however, the FFF technique is used to fabricate highly dense alumina parts of different designs (cylinders, bars, and pillars) using a low-cost desktop 3D printer. To investigate the repeatability and reliability of filament-based 3D printing, micro-computed tomography (micro-CT) was applied to analyse and evaluate critical processing steps during the preparation of high dense alumina ceramics. Results of non-destructive testing showed that the 3D sample shape plays a significant role in defect-free printouts, and the printed errors and deviations increased in the direction from cylindrical to prismatic objects. Despite internal cracks, pores and cavities revealed by the micro-CT analysis, sintered objects achieved relative densities of up to 96%.

Qualitative and quantitative quality evaluation of black tea fermentation through noncontact chemical imaging

Fermentation is the most critical step in black tea processing and contributes to the taste and appearance of black tea. Conventionally, the degree of fermentation is evaluated through sensory reviews by experts. However, this method has low accuracy and is subject to reviewers’ experience. In this study, the fermentation quality of black tea was qualitatively and quantitatively evaluated through near-infrared hyperspectral imaging (HSI). The spectral information of samples of four fermentation degrees was collected and preprocessed using standard normal variate. The developed qualitative model could discriminate fermentation degrees, with a correct classification rate of 83.33 % for the prediction set. The quantitative prediction model achieved excellent prediction accuracy for the catechin content, with a residual prediction deviation of 2.18. Finally, the spatial distribution of catechins during fermentation was mapped through chemical imaging. Our study confirmed the feasibility of HSI for monitoring the fermentation quality of black tea.

Safety assessment of the process deSter, used to recycle plastic catering tableware for use as food contact materials.

The EFSA Panel on Food Contact Materials, Enzymes and Processing Aids (CEP) assessed the safety of the recycling process deSter (EU register number RECYC196). The input consists of catering tableware food contact articles from airline on‐board services, made of polypropylene (PP), polyethylene terephthalate (PET), styrene acrylonitrile resin (SAN) or acrylonitrile butadiene styrene (ABS). The recyclate produced by deSter will be used to manufacture articles for the same on‐board services. The Panel considered that the management system put in place to ensure compliance of the origin of the input with Commission Regulation (EC) No 282/2008 and to provide full traceability from input to final product is the critical process step. The process deSter uses only materials and articles intended for food contact and ensures that any contamination can be ruled out, since the input originates from this product loop managed in a closed and controlled chain. Therefore, the Panel concluded that the recycled materials obtained from this process and used within this loop are not of safety concern, when used at up to 100% for the manufacture of plastic tableware for contact with all types of foodstuffs under the conditions of use of the articles before recycling.

(Invited) Synergistic Effect of Polyvinyl Alcohol (PVA) Brush Type and Cleaning Chemistry Formulation for Copper (Cu) Post-Chemical Mechanical Planarization (p-CMP) Cleaning

Chemical Mechanical Planarization (CMP) is a critical process step in extending Moore’s Law and must be understood at a deeper, mechanistic level to limit defects that are detrimental to shrinking feature size. Specifically, Cu CMP utilizes a nanoparticle dispersion (slurry) composed of SiO2 abrasive nanoparticles, complexing agent, oxidizer, and a corrosion inhibitor. Slurry components must work in synergy to chemically modify the Cu surface while unwanted topography is removed by mechanical abrasion. The CMP process can cause various defects, and they can be classified as mechanical (i.e., scratching), chemical (i.e., corrosion), or physiochemical (i.e., adsorbed contaminants) according to the mechanism of formation. Adsorbed particles, polish residues, pad debris, and other foreign materials can have catastrophic impacts on device performance as line width decreases to the atomic scale. Upon the completion of the CMP process, removal of unwanted particle residue/organic contaminants is achieved using oxidation/reduction reactions, etching, or encapsulation chemistry coupled with ultrasonic methods or polyvinyl alcohol (PVA) brush cleaning. The nature of the current cleaning techniques has been known to provide insufficient cleaning capacity for next generation devices. This presentation will explore two fundamentally different approaches to post-CMP cleaning for Cu processes. First, the implementation of supramolecular cleaning chemistries to traditional PVA brush scrubbing was explored for the removal of SiO2 nanoparticles from a Cu surface. This work explored a structure activity relationship between the type of supramolecular cleaning chemistry used and its ability to effectively “encapsulate” nanoparticle contaminants. Initial results indicate that the anionic and non-ionic micelle show some corrosive behavior due to non-uniform passivation layers formed at the Cu surface during cleaning. This behavior is not seen with the cationic micelle suggesting its effective surface adsorption to the contaminant particle which can ultimately be removed via brush contact. Secondly, current cleaning chemistries typically utilize an undercutting mechanism to remove organic residues (i.e., BTA), however, this requires harsh oxidation and can result in scratches/erosion. Therefore, an approach to chemically activate the residue in an “overcutting” mechanism can improve cleaning performance with reduced defectivity. More specifically, this work will explore the use of α-β-unsaturated ketones as cleaning agents to initiate the nucleophilic attack and subsequent removal of BTA via a Michael’s Addition reaction. This mechanism will be evaluated via dynamic Tafel analysis, contact angle, atomic force microscopy, and cleaning performance to correlate interfacial interactions/reactions to p-CMP performance.

An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

Extrusion and injection moulding induced degradation of date palm fibre - polypropylene composites

The use of date palm fibres, which are an abundant by-product of agriculture, as natural reinforcement in polypropylene (PP) was studied. Because odour emissions are one of the major challenges for natural fibre-based composites, the emission of Volatile Organic Compounds (VOC) generated at different processing steps was analysed online. The severest degradation on the date palm fibre morphology was observed at the injection moulding step, but even when degradation occurred date palm fibres were an effective reinforcement of PP and increased the Young's modulus significantly. Generation of odorous compounds caused by thermal degradation of date palm fibres such as furfural alcohol or methyl furfural was astonishingly low during the twin-screw extrusion and granulation process. In coherence with the evolution of the fibre morphology, the most critical processing step was the injection process. In conclusion, date palm fibres are a promising natural fibre for the production of natural fibre-based composites. Adapted twin-screw extrusion conditions allow for minimizing VOC emissions, but particular attention needs to be given to the optimization of the injection process.

Development of a Stimuli-Responsive Supramolecular Brush for Enhanced Post-CMP Cleaning

With the miniaturization of device features, slurry chemistries have become increasingly complex and therefore require enhancement of current cleaning processes. Copper (Cu) chemical mechanical planarization (CMP), a critical process step in the fabrication of high-performance microprocessors, utilizes nanoparticle abrasives (i.e. SiO2) and functional chemistry (i.e. complexing agents, inhibitors, and pH adjusters) during the polishing process which can lead to contaminants on the wafer surface. Currently, post-CMP cleaning processes use a standard polyvinyl alcohol (PVA) brush coupled with functional cleaning chemistry to remove these contaminants, including organic residue, particle contaminants, and excess ions. With stringent contaminant-free demands, it is crucial to develop cleaning consumable sets to meet these demands. This work focuses on the functionalization of PVA with supramolecular moieties to enhance the removal of nanoparticle and organic residue. Results have shown that the addition of supramolecular complexes to the PVA brush enhanced particle removal through hydrophobic/hydrophilic interactions at the brush/wafer interface. Additionally, this functionalized PVA brush shows increased adsorption of metal ions (specifically Cu2+) via inclusion complexes. Initial results have also shown that PVA containing pore forming additives have a photochemical response in which when irradiated with specific wavelengths of light the brush can be turned “on” or “off” based on chemical interactions at the brush/wafer interface.

(Invited) Reimaging the CMP Process Utilizing Photoactive Slurry Formulations

Chemical Mechanical Planarization (CMP) has emerged as a critical process step for achieving angstrom-level uniformity in advanced integrated circuit manufacturing and has ultimately led to the extension of Moore’s Law. Due to the emergence of these sub-7 nm technologies, next generation CMP slurry formulations have continued to increase in additive complexity to meet stringent device specifications. Currently, the most common processing condition to modulate removal rate is the use of mechanical parameters (i.e. downforce, flow rate, and platen speed). However, these parameters may manifest in increased defectivity (i.e. scratches and dishing/erosion) due to increased shear force/friction and chemical activity. This work focuses on the utilization of photo switching mechanisms to tune the slurry nanoparticle surface to enhance its electrophilicity and ultimately increase the rate of removal upon irradiation. More specifically, this presentation will discuss two modes of photo-switching to selectively modulate oxide removal rates. One mode exploits a photo-induced adsorption/desorption mechanism to alter the oxygen vacancy availability to provide an “off/on” switch for removal. While another mode utilizes a light induced ligand-metal charge transfer (LMCT) in which the particle is reduced and the functional additive is desorbed allowing for greater oxygen vacancies as well as less steric hinderance.

First Arrival Picking on Microseismic Signals Based on K-Means with a ReliefF Algorithm

The quick and accurate picking of the first arrival on microseismic signals is one of the critical processing steps of microseismic monitoring. This study proposed a first arrival picking method for application to microseismic data with a low signal-to-noise ratio (SNR). This approach consisted of two steps: feature selection and clustering. First of all, the optimal feature was searched automatically using the ReliefF algorithm according to the weight distribution of the signal features, and without manual design. On that basis, a k-means clustering method was adopted to classify the microseismic data with symmetry (0–1), and the first arrival times were accurately picked. The proposed method was validated using the synthetic data with different noise levels and real microseismic data. The comparative study results indicated that the proposed method had obviously outperformed the classical STA/LTA and the k-means without feature selection. Finally, the microseismic localization of the first arrivals picked using the various methods were compared. The positioning errors were analyzed using box plots with symmetric effect, and those of the proposed method were the smallest, and stable (all of which were less than 0.5 m), which further verified the superiority of this study’s proposed method and its potential in processing complicated microseismic datasets.

Tool geometry analysis for plunge milling of lead-free CuZn-alloys

Plunge milling is a critical process step in mass manufacturing of rectangular shapes in electrical connector components. These shapes are manufactured by drilling a pilot hole and subsequent plunge milling with a radial offset (pitch) one or more times. The plunged cavity serves as guidance for the final broaching cut. In light of new legislative initiatives, the electronics industry is forced to use lead-free Cu-Zn-Alloys for mass manufacturing of these connectors. The plunging tool is deflected due to the higher cutting forces experienced in machining of lead-free CuZn-alloys in comparison to alloys with lead. This results in an offset of the milled cavity and negatively impacts tool guidance in the subsequent broaching process. Therefore, the geometric tolerances cannot be met. In this paper, the effect of tool geometry and cutting parameters on the workpiece geometry in plunge milling is investigated. The effect of the microstructure of the work-piece materials CuZn37, CuZn42 and CuZn21Si3P on the tool deflection and cutting force components is examined. The tools used vary regarding the design of the corner in terms of the corner chamfer and the inner shaft thickness. Friction between chips in the tools inner flutes and the cavity walls reduced workpiece accuracy. Improvements were achieved by reducing the width of the cutting corner chamfers, using large inner flutes and applying low cutting parameters.

Development of a scalable and robust AEX method for enriched rAAV preparations in genome-containing VCs of serotypes 5, 6, 8, and 9

Removal of empty capsids from adeno-associated virus (AAV) manufacturing lots remains a critical step in the downstream processing of AAV clinical-grade batches. Because of similar physico-chemical characteristics, the AAV capsid populations totally lacking or containing partial viral DNA are difficult to separate from the desired vector capsid populations. Based on minute differences in density, ultracentrifugation remains the most effective separation method and has been extensively used at small scale but has limitations associated with availabilities and operational complexities in large-scale processing. In this paper, we report a scalable, robust, and versatile anion-exchange chromatography (AEX) method for removing empty capsids and subsequent enrichment of vectors of AAV serotypes 5, 6, 8, and 9. On average, AEX resulted in about 9-fold enrichment of AAV5 in a single step containing 80% ± 5% genome-containing vector capsids, as verified and quantified by analytical ultracentrifugation. The optimized process was further validated using AAV6, AAV8, and AAV9, resulting in over 90% vector enrichment. The AEX process showed comparable results not only for vectors with different transgenes of different sizes but also for AEX runs under different geometries of chromatographic media. The herein-reported sulfate-salt-based AEX process can be adapted to different AAV serotypes by appropriately adjusting elution conditions to achieve enriched vector preparations.

Cl-Containing Microplastics from the Environment

One of the critical process steps in the assembly of integrated circuits is the making of the connection between the silicon chip and the outside world. This is done by a thermo-sonic welding process of sometimes more than 800 copper wires to the aluminum bond pads. The Al-Cu intermetallic compound formed during this process is sensitive for trace amount of chloride from the environment. It is found that Cl-containing microplastics are the source of this chloride and further analyzing these particles helps to identify the origin of these particles.

Effects of conventional processing methods on whey proteins in production of native whey powder

Native whey, produced by microfiltration of skim milk, has great potential in infant nutrition and as a functional food ingredient; however, processes to produce whey powder using high temperatures can adversely affect protein quality. The individual effects of three critical processing steps were investigated: standard thermal pasteurisation, membrane concentration and spray drying on protein quality when performed under the mildest conditions considered feasible for industrial operation. HPLC-analysis was used to measure the degree of denaturation of the six most abundant whey proteins (β-lactoglobulin, α-lactalbumin, bovine serum albumin, immunoglobulins, lactoferrin and lactoperoxidase) before and after each processing step. Overall, denaturation was minimal throughout our tests, although the tendency to denature was unequal between the individual proteins and also varied between tested processing types and medium compositions. In the production of native whey with microfiltration, retentions varied greatly between the proteins but were not affected by pasteurisation of milk.

Establishing functional lentiviral vector production in a stirred bioreactor for CAR-T cell therapy

ABSTRACT As gene delivery tools, lentiviral vectors (LV) have broad applications in chimeric antigen receptor therapy (CAR-T). Large-scale production of functional LV is limited by the adherent, serum-dependent nature of HEK293T cells used in the manufacturing. HEK293T adherent cells were adapted to suspension cells in a serum-free medium to establish large-scale processes for functional LV production in a stirred bioreactor without micro-carriers. The results showed that 293 T suspension was successfully cultivated in F media (293 CD05 medium and SMM293-TII with 1:1 volume ratio), and the cells retained the capacity for LV production. After cultivation in a 5.5 L bioreactor for 4 days, the cells produced 1.5 ± 0.3 × 107 TU/mL raw LV, and the lentiviral transduction efficiency was 48.6 ± 2.8% in T Cells. The yield of LV equaled to the previous shake flask. The critical process steps were completed to enable a large-scale LV production process. Besides, a cryopreservation solution was developed to reduce protein involvement, avoid cell grafting and reduce process cost. The process is cost-effective and easy to scale up production, which is expected to be highly competitive.

Drying Performance of Coffee in a Batch-Type Heat Pump Dryer

<b>Highlights</b> <list list-type=bullet><list-item> The SMER<i>_hp</i> and SMER<i>_ws</i> values for this heat pump drying system were 0.960 and 0.719 kg kWh^-1, respectively. </list-item><list-item> A non-heating period in the heat pump dryer working cycle may affect the asynchronous negative drying rates of coffee in different layers in the drying chamber. </list-item><list-item> Results indicated that ambient air conditions might affect drying behavior in an open-loop batch-type heat pump dryer. </list-item></list> <b>Abstract</b>. Coffee is an important commercial crop in the world due to its popular flavor and aroma. The critical step in coffee processing is drying, which determines the coffee quality and market value. The most popular coffee drying techniques at present, sun drying and mechanical drying, have inherent disadvantages. In contrast, heat pump drying has been observed to offer higher energy efficiency and produce higher-quality output. Therefore, the objective of this research was to investigate the drying characteristics of coffee in a batch-type heat pump dryer. A total of 370 kg (0.72 m3) of fresh coffee at 70.01% ±1.26% (w.b.) moisture content was dried in an open-loop batch-type heat pump dryer. Samples were collected from six different horizontal layers in the drying chamber and two vertical sampling points. The average moisture content of three replicates was recorded. Wet basis and dry basis moisture content, drying rate, moisture ratio, color, temperature, and relative humidity of the ambient air and outflow air were measured and recorded. The SMERhp and SMERws values for this heat pump drying system were 0.960 and 0.719 kg kWh-1, respectively. The ΔE value for coffee at 10.11% ±0.42% (w.b.) moisture content was 31.65. These results indicate that the ambient air conditions may affect drying behavior in an open-loop batch-type heat pump dryer. Further studies are needed to identify the impact on aroma and flavor when processing coffee in a heat pump drying system to rationalize the advantages of using heat pump drying for coffee.

Influence of Process Variations on the Electrical Performance of SiC Power MOSFETs

The silicon carbide (SiC) power MOSFET is a promising solution for power electronics applications demanding high energy efficiency and high power density. For high-current applications, typically several transistors are operated in parallel, requiring synchronous switching. Commercial SiC power MOSFETs may, however, still show a significant performance spread in turn-on electrical behavior and, hence, lifetime in such a system. Based on a calibrated TCAD model of a commercial 1.2-kV SiC MOSFET, the impact of variations on the critical processing steps is evaluated. Controlling the epitaxial doping concentration as well as the interface trap density is most important for a low device-to-device variability. Furthermore, it is shown that the improvement of the output performance due to an increasing channel mobility quickly saturates.

Exploring Inherently Selective Plasma Processing of Nitrides and Oxides Via Hollow-Cathode Plasma-Assisted Atomic Layer Deposition

Area-selective atomic layer deposition (AS-ALD) is an emerging field of research attracting accelerating interest from the CMOS materials and device research community. The continuing down-scaling in logic and memory field-effect transistor devices along with the increasing three-dimensional structural complexity brings new patterning and processing challenges. With the long-awaited adoption of extreme-UV (EUV) lithography, the escalating stress on precision patterning has decreased, however, alternative self-aligned processing solutions are always highly needed to reduce the number of lithography/deposition/etching steps. AS-ALD, therefore, provides a unique opportunity towards simplifying critical process steps where surface-selective and/or conformal material deposition with sub-nanometer thickness precision is required.Being introduced in the early years of the 21st century, during the early adoption of ALD-grown high-k dielectrics in the CMOS manufacturing industry, significant progress has been made in developing alternative routes for AS-ALD of various oxide and nitride compounds, as well as elemental metallic films. Nucleation/growth inhibition on various surfaces including Si, oxide, nitride, and metal substrates have been explored extensively. The most promising results have been achieved via various self-assembled monolayers (SAMs), which serves as the masking layer to protect the non-growth surface and can be removed with relative ease after ALD process has been completed. Despite successful results for a set of materials grown via conventional thermal-ALD, SAMs are not compatible with plasma-ALD processes, mainly because their soft organic structure which is easily etched under energetic plasma exposure. Therefore, there is a significant need to develop alternative AS-ALD strategies for materials which require the utilization of energetic plasmas for their deposition.While certain nitride materials necessitate the usage of various nitrogen plasmas including III-nitrides for their self-limiting growth at low substrate temperatures, plasma-assistance helps oxides as well in lowering their deposition temperatures and improving physical properties of the films. Previously, we have reported extensively on the self-limiting plasma-assisted ALD (PA-ALD) of crystalline III-nitride films within a common substrate temperature window of lower than 250 °C. In this study, we explore the feasibility of developing area-selective deposition recipes for nitride and oxide compounds via inherently selective nucleation/growth behavior on different surfaces.Our initial results belong to two oxide (Ga2O3 and VOx) and one nitride (BN) binary compound films, all deposited in the same PA-ALD system featuring a large-diameter hollow-cathode plasma source. Ga2O3, VOx, and BN films were deposited using triethylgallium (TEG), TEMAV, and triethylboron (TEB) as metal precursors, respectively, while Ar/O2 and Ar/N2 plasma were used as oxygen and nitrogen co-reactants, respectively. Growth experiments have been performed at 200 °C substrate temperature and within 50 – 100 W rf-power range. Real-time in-situ ellipsometry was used to closely monitor the individual growth reactions taking place on the surface, depicting metal precursor chemisorption and plasma-assisted ligand removal/exchange reactions. 30-cycle deposition runs on three different substrate surfaces (solvent-cleaned Si, BOE-cleaned Si, and solvent-cleaned Al2O3) were in-situ monitored for the detection of any nucleation inhibition or growth retardation.Our initial results show that inherent surface selectivity in PA-ALD growth of VOx and BN films on Si vs. Al2O3 substrates can be achieved. Our in-situ ellipsometry results indicate nucleation inhibition behavior of up to 15 ALD cycles for BN on Al2O3 surfaces, when compared to almost no retardation on both Si substrates. Moreover, we also observed that pre-deposition surface plasma treatments result in significantly different growth rates on Si surfaces, with the fastest growth occurring on non-plasma treated surfaces. Pretty similar results were obtained for VOx films, showing a rather significant nucleation delay of at least 25 cycles on Al2O3 surfaces, while plasma-treated Si surfaces show differences in growth behavior. These initial results confirm indeed the feasibility of achieving selective plasma processing, despite the known rather unselective behavior of energetic plasmas. We attribute these initial promising results to the radical-rich content of our hollow-cathode plasma which most probably shows more surface chemistry dependence when compared to ion-rich plasmas. We’ll present ex-situ material characterization data of thicker films along with possible routes to obtain desired higher selectivity values. Figure 1

Temporal Convolutional Network Connected with an Anti-Arrhythmia Hidden Semi-Markov Model for Heart Sound Segmentation

Heart sound segmentation (HSS) is a critical step in heart sound processing, where it improves the interpretability of heart sound disease classification algorithms. In this study, we aimed to develop a real-time algorithm for HSS by combining the temporal convolutional network (TCN) and the hidden semi-Markov model (HSMM), and improve the performance of HSMM for heart sounds with arrhythmias. We experimented with TCN and determined the best parameters based on spectral features, envelopes, and one-dimensional CNN. However, the TCN results could contradict the natural fixed order of S1-systolic-S2-diastolic of heart sound, and thereby the Viterbi algorithm based on HSMM was connected to correct the order errors. On this basis, we improved the performance of the Viterbi algorithm when detecting heart sounds with cardiac arrhythmias by changing the distribution and weights of the state duration probabilities. The public PhysioNet Computing in Cardiology Challenge 2016 data set was employed to evaluate the performance of the proposed algorithm. The proposed algorithm achieved an F1 score of 97.02%, and this result was comparable with the current state-of-the-art segmentation algorithms. In addition, the proposed enhanced Viterbi algorithm for HSMM corrected 30 out of 30 arrhythmia errors after checking one by one in the dataset.