Cluster Tool Research Articles

An Efficient Scheduling Method for Single-Arm Cluster Tools With Multifunctional Process Modules

Nowadays, cluster tools are extensively used for many wafer manufacturing processes, such as coating, lithograph, developing, etching, deposition, and testing. Traditional process modules in cluster tools can execute a single operation only. With the rapid development of equipment design, multifunctional process modules (MPMs) are equipped to serve for processing multiple operations together just like a single operation. With different wafer processing parameters, MPMs may be set for processing multiple operations together or processing just a single operation to form different schedules so as to maximize the productivity. Thus, it is highly desired to find an efficient scheduling method to quickly adapt to wafer processing parameter changes for productivity maximization by taking the advantages of MPMs. To tackle this issue, a deadlock-free Petri net (PN) model is developed to describe the behavior of a single-arm cluster tool. Based on the evolving mechanism of the PN model, two algorithms are developed to calculate the makespan for completing a given number of wafers. Then, an adaptive scheduling method is presented to set the functions of MPMs to minimize the makespan. Finally, experimental results show the efficiency and effectiveness of the proposed method.

Dual-Arm Cluster Tool Scheduling for Reentrant Wafer Flows

Cluster tools are the key equipment in semiconductor manufacturing systems. They have been widely adopted for many wafer fabrication processes, such as chemical and physical vapor deposition processes. Reentrant wafer flows are commonly seen in cluster tool operations for deposition processes. It is very complicated to schedule cluster tools with reentrant processes. For a dual-arm cluster tool with two-time reentering, the existing studies point out that a one-wafer periodical (1-WP) schedule can be found, and it is optimal in terms of productivity. However, for some wafer fabrication processes, wafers should be processed at some PMs more than two times. This gives rise to a question of whether there still exists a 1-WP schedule for dual-arm cluster tools with the number of reentering times being more than two such that the cycle time of a tool can reach the lower bound. This problem is still open, and this is what this work wants to tackle. For a dual-arm cluster tool with the number of reentering times being k (>2) times, if there does not exist a value f ∈ {1, 2 …} such that k = 3f, theoretical proofs are given to show that a 1-WP schedule can be found, otherwise it does not exist. For cases with a 1-WP schedule, the cycle time can be obtained by analytical expressions. For the cases without a 1-WP schedule, two new methods for a three-wafer periodical schedule are proposed to improve the system productivity by comparing it with an existing three-wafer periodical schedule. The applications of the obtained results are demonstrated by examples. Wafer residency time constraints are required for some wafer fabrication processes. Note that the results obtained in this work cannot be directly applied to cluster tools with both reentrant wafer flows and wafer residency time constraints. Nevertheless, schedulablity and scheduling analyses for that applications can be conducted based on the obtained results in this work.

RabbitTClust: enabling fast clustering analysis of millions of bacteria genomes with MinHash sketches

We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences from RefSeq, 455 GB in FASTA format, can be clustered within less than 6 min and 1,009,738 GenBank assembled bacterial genomes, 4.0 TB in FASTA format, within only 34 min on a 128-core workstation. Our results further identify 1269 redundant genomes, with identical nucleotide content, in the RefSeq bacterial genomes database.

HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space.

As current shotgun proteomics experiments can produce gigabytes of mass spectrometry data per hour, processing these massive data volumes has become progressively more challenging. Spectral clustering is an effective approach to speed up downstream data processing by merging highly similar spectra to minimize data redundancy. However, because state-of-the-art spectral clustering tools fail to achieve optimal runtimes, this simply moves the processing bottleneck. In this work, we present a fast spectral clustering tool, HyperSpec, based on hyperdimensional computing (HDC). HDC shows promising clustering capability while only requiring lightweight binary operations with high parallelism that can be optimized using low-level hardware architectures, making it possible to run HyperSpec on graphics processing units to achieve extremely efficient spectral clustering performance. Additionally, HyperSpec includes optimized data preprocessing modules to reduce the spectrum preprocessing time, which is a critical bottleneck during spectral clustering. Based on experiments using various mass spectrometry data sets, HyperSpec produces results with comparable clustering quality as state-of-the-art spectral clustering tools while achieving speedups by orders of magnitude, shortening the clustering runtime of over 21 million spectra from 4 h to only 24 min.

Unsupervised Deep Learning Can Identify Protein Functional Groups from Unaligned Sequences.

Interpreting protein function from sequence data is a fundamental goal of bioinformatics. However, our current understanding of protein diversity is bottlenecked by the fact that most proteins have only been functionally validated in model organisms, limiting our understanding of how function varies with gene sequence diversity. Thus, accuracy of inferences in clades without model representatives is questionable. Unsupervised learning may help to ameliorate this bias by identifying highly complex patterns and structure from large datasets without external labels. Here we present DeepSeqProt, an unsupervised deep learning program for exploring large protein sequence datasets. DeepSeqProt is a clustering tool capable of distinguishing between broad classes of proteins while learning local and global structure of functional space. DeepSeqProt is capable of learning salient biological features from unaligned, unannotated sequences. DeepSeqProt is more likely to capture complete protein families and statistically significant shared ontologies within proteomes than other clustering methods. We hope this framework will prove of use to researchers and provide a preliminary step in further developing unsupervised deep learning in molecular biology.

Robust reservoir identification by multi-well cluster analysis of wireline logging data

A novel clustering method is applied to well logs for improved rock type identification in hydrocarbon formations. For grouping the objects in the multi-dimensional data space, we propose a Most Frequent Value (MFV) based clustering technique applied to natural gamma ray, bulk density, sonic, photoelectric index, and resistivity logs. The MFV method is a robust estimator, which assists in finding the cluster centers more reliably than a more noise sensitive K-means clustering approach. The result of K-means cluster analysis highly depends on the choose of the initial centroids. To reduce the risk of inappropriately chosen starting values, we apply a histogram-based selection method to give the best position of the initial cluster centers. We assure the robustness of the solution by calculating the centroid as the MFV of the cluster elements and defining the overall deviation of cluster elements from the center by a weighted Euclidean (Steiner-) distance. The proposed workflow relies on a fully automated weighting of the cluster elements, which does not require a constraint on the statistical distribution of the observed variables. The processing of synthetic data shows high noise rejection capability and efficient cluster recognition even beside considerable amount of outlying and missing data; the accuracy is measured by the difference between the estimated and the exactly known distribution of cluster numbers. The clustering tool is first applied to single borehole data, then the procedure is extended to multi-well logging datasets to reconstruct the multi-dimensional spatial distributions of clusters revealing the lithological and petrophysical characteristics of the studied formations. A large in situ dataset acquired from several boreholes traversing Hungarian gas-bearing clastic reservoirs of Miocene age is analyzed. The accuracy of the field results is confirmed by core permeability measurements, independent well log analysis and a gradient metrics characterizing the noise rejection capability of the clustering method.

Comparative evaluation of the MAPlex, Precision ID Ancestry Panel, and VISAGE Basic Tool for biogeographical ancestry inference

Biogeographical ancestry (BGA) inference from ancestry-informative markers (AIMs) has strong potential to support forensic investigations. Over the past two decades, several forensic panels composed of AIMs have been developed to predict ancestry at a continental scale. These panels typically comprise fewer than 200 AIMs and have been designed and tested with a limited set of populations. How well these panels recover patterns of genetic diversity relative to larger sets of markers, and how accurately they infer ancestry of individuals and populations not included in their design remains poorly understood. The lack of comparative studies addressing these aspects makes the selection of appropriate panels for forensic laboratories difficult. In this study, the model-based genetic clustering tool STRUCTURE was used to compare three popular forensic BGA panels: MAPlex, Precision ID Ancestry Panel (PIDAP), and VISAGE Basic Tool (VISAGE BT) relative to a genome-wide reference set of 10k SNPs. The genotypes for all these markers were obtained for a comprehensive set of 3957 individuals from 228 worldwide human populations. Our results indicate that at the broad continental scale (K=6) typically examined in forensic studies, all forensic panels produced similar genetic structure patterns compared to the reference set (G'≈90%) and had high classification performance across all regions (average AUC-PR > 97%). However, at K=7 and K=8, the forensic panels displayed some region-specific clustering deviations from the reference set, particularly in Europe and the region of East and South-East Asia, which may be attributed to differences in the design of the respective panels. Overall, the panel with the most consistent performance in all regions was VISAGE BT with an average weighted AUC̅W score of 96.26% across the three scales of geographical resolution investigated.

Optimal Cyclic Scheduling of Wafer-Residency-Time-Constrained Dual-Arm Cluster Tools by Configuring Processing Modules and Robot Waiting Time

Optimal cyclic scheduling problems of wafer-residency-time-constrained dual-arm cluster tools in wafer fabrication are challenging and remain to be fully solved. Existing studies assume that all processing modules (PMs) of a required type are used to process the same type of wafers. This sometimes brings unneeded conservation to scheduling results, because we may be able to make a tool schedulable by reducing the number of PMs in some steps if the original one is not. In some cases, we may use fewer PMs to reach the same result if the original one is schedulable. This work selects a proper number of PMs of needed types to process wafers with the highest productivity. It proposes the necessary and sufficient conditions under which a tool is schedulable and develops a polynomial-complexity algorithm that finds an optimal cyclic schedule. Examples are given to show its superiority over existing ones, thus advancing this field greatly.

The Use of Ensemble Clustering on a Multimodel Ensemble for Medium-Range Forecasting at the Weather Prediction Center

Abstract This article introduces an ensemble clustering tool developed at the Weather Prediction Center (WPC) to assist forecasters in the preparation of medium-range (3–7 day) forecasts. Effectively incorporating ensemble data into an operational forecasting process, like that used at WPC, can be challenging given time constraints and data infrastructure limitations. Often forecasters do not have time to view the large number of constituent members of an ensemble forecast, so they settle for viewing the ensemble’s mean and spread. This ignores the useful information about forecast uncertainty and the range of possible forecast outcomes that an ensemble forecast can provide. Ensemble clustering could be a solution to this problem as it can reduce a large ensemble forecast down to the most prevalent forecast scenarios. Forecasters can then quickly view these ensemble clusters to better understand and communicate forecast uncertainty and the range of possible forecast outcomes. The ensemble clustering tool developed at WPC is a variation of fuzzy clustering where operationally available ensemble members with similar 500-hPa geopotential height forecasts are grouped into four clusters. A representative case from 15 February 2021 is presented to demonstrate the clustering methodology and the overall utility of this new ensemble clustering tool. Cumulative verification statistics show that one of the four forecast scenarios identified by this ensemble clustering tool routinely outperforms all the available ensemble mean and deterministic forecasts. Significance Statement Ensemble forecasts could be used more effectively in medium-range (3–7 day) forecasting. Currently, the onus is put on forecasters to view and synthesize all of the data contained in an ensemble forecast. This is a task they often do not have time to adequately execute. This work proposes a solution to this problem. An automated tool was developed that would split the available ensemble members into four groups of broadly similar members. These groups were presented to forecasters as four potential forecast outcomes. Forecasters felt this tool helped them to better incorporate ensemble forecasts into their forecast process. Verification shows that presenting ensemble forecasts in this manner is an improvement on currently used ensemble forecast visualization techniques.

Evaluation of chemical grouping workflows for flavor inhalation risk assessment: Selected furan moiety-containing chemicals as a case study

Read-across and chemical grouping approaches are increasingly utilized in risk assessment and regulatory submissions. The European Food Safety Authority (EFSA) has established Chemical Group (CG) 14, which contains furfuryl and furan derivatives both with and without side-chain substituents; however, the rationale for this grouping is not available based on current EFSA documentation. Therefore, this study aimed to identify the chemicals belonging to CG14, evaluate the constituent chemicals for metabolic, biological, and toxicological properties via clustering tools, and apply existing chemical grouping workflows for identifying representative chemicals for this group to support testing strategies. Membership to CG14 was difficult to identify, and varied by EFSA source (e.g., published reports, OpenFoodTox database). Based on predictions from the Organisation for Economic Co-operation and Development (OECD) Quantitative Structure Activity Relationship (QSAR) Toolbox, ChemACE, SMARTCyp, and WhichCyp, as well as data extracted from the U.S. Environmental Protection Agency’s (EPA’s) Toxicity Forecaster (ToxCast) on CompTox Chemicals Dashboard and the European Chemicals Agency (ECHA) Dossier, no suitable metabolic, toxicological, structural, or mechanistic clustering method was identified for CG14. Biological effect data were too sparse to refine the chemical subgroupings within CG14 with confidence based on existing read-across principles for chemical grouping. This paucity of data limits the development of a tiered testing strategy in which more complete testing would be conducted for selected representative CG14 compounds only. Therefore, efforts to generate key pieces of data (e.g., mode of action [MOA], metabolism) for chemical grouping and read-across are needed to apply this workflow to EFSA CG14.

Application of fuzzy C-means clustering and fuzzy EDAS to assess groundwater irrigation suitability and prioritization for agricultural development in a complex hydrogeological basin.

The current research is focused on detecting a river basin suitable for agriculture and priority for management using a new clustering tool of groundwater quality with fuzzy logic technique in R and Geographical Information System. A new fuzzy clustering-soft computing technique has been executed to determine the different hydrochemical zones considering 13 essential parameters such as electrical conductivity, hardness, chloride, sodium adsorption ratio, residual sodium carbonate, soluble sodium percent, magnesium hazard, permeability index, potential salinity, residual sodium bicarbonate, Kelly's ratio, synthetic harmful coefficient, and exchangeable sodium percentage. The derived fuzzy C-mean clustering (FCM) outperformed other available hard computing techniques like hierarchical clustering, K-means clustering, and agglomerative clustering. It divided the sampling sites into 2 clustering groups (FCM I and FCM II) which has been validated using fuzzy silhouette index (0.85), the partition coefficient (0.76), the partial entropy (0.68), and the modified partition coefficient (0.52). The hydrogeochemical analysis confirmed that the rock-water interaction, chemical weathering, and ion exchange process are predominant in the aquifer system of the study area. According to the correlation plots, the studied groundwater samples largely evolved from [Formula: see text], mixed [Formula: see text] types, and [Formula: see text] types. The spatial distribution map and the hydrochemical analysis also gives a clear depiction of the fluoride (> 1.0mg/l) and high iron (> 0.3mg/l) contamination in groundwater quality, making it unsuitable for both drinking and irrigation. A fuzzy EDAS priority map has been prepared based on all the irrigation suitability parameters which concludes that the groundwater at the upstream and downstream section of the basin requires the most attention. Based on the highest priority for management, five zones have been delineated: very high (5.98%), high (22.31%), medium (16.39%), low (32.30%), and very low (23.02). The findings of this study will be beneficial to planners and policymakers as they can develop schemes to solve similar problems across the country.

Control laws synthesis for timed event graphs subject to generalised marking constraints by Min-Plus algebra: application to cluster tools

This paper discusses the problem of control laws for Discrete Event Dynamic Systems (DEDSs) represented by Timed Event Graphs (TEGs) under Generalized Marking Constraints (GMCs). The behaviour of TEGs is described using linear equations while GMCs are expressed by weighted inequalities in Min-Plus algebra. We formulate the problem in terms of control linear Min-Plus models. Hence, an algebraic method to calculate control laws ensuring the respect of GMCs is suggested. And once sufficient conditions are satisfied, we propose casual feedbacks to guarantee these marking specifications. Besides, the proposed control strategy is applied to a dual-armed cluster tool, a well-known industrial application.

Myocardial Metabolomics of Human Heart Failure With Preserved Ejection Fraction.

The human heart primarily metabolizes fatty acids, and this decreases as alternative fuel use rises in heart failure with reduced ejection fraction (HFrEF). Patients with severe obesity and diabetes are thought to have increased myocardial fatty acid metabolism, but whether this is found in those who also have heart failure with preserved ejection fraction (HFpEF) is unknown. Plasma and endomyocardial biopsies were obtained from HFpEF (n=38), HFrEF (n=30), and nonfailing donor controls (n=20). Quantitative targeted metabolomics measured organic acids, amino acids, and acylcarnitines in myocardium (72 metabolites) and plasma (69 metabolites). The results were integrated with reported RNA sequencing data. Metabolomics were analyzed using agnostic clustering tools, Kruskal-Wallis test with Dunn test, and machine learning. Agnostic clustering of myocardial but not plasma metabolites separated disease groups. Despite more obesity and diabetes in HFpEF versus HFrEF (body mass index, 39.8 kg/m2 versus 26.1 kg/m2; diabetes, 70% versus 30%; both P<0.0001), medium- and long-chain acylcarnitines (mostly metabolites of fatty acid oxidation) were markedly lower in myocardium from both heart failure groups versus control. In contrast, plasma levels were no different or higher than control. Gene expression linked to fatty acid metabolism was generally lower in HFpEF versus control. Myocardial pyruvate was higher in HFpEF whereas the tricarboxylic acid cycle intermediates succinate and fumarate were lower, as were several genes controlling glucose metabolism. Non-branched-chain and branched-chain amino acids (BCAA) were highest in HFpEF myocardium, yet downstream BCAA metabolites and genes controlling BCAA metabolism were lower. Ketone levels were higher in myocardium and plasma of patients with HFrEF but not HFpEF. HFpEF metabolomic-derived subgroups were differentiated by only a few differences in BCAA metabolites. Despite marked obesity and diabetes, HFpEF myocardium exhibited lower fatty acid metabolites compared with HFrEF. Ketones and metabolites of the tricarboxylic acid cycle and BCAA were also lower in HFpEF, suggesting insufficient use of alternative fuels. These differences were not detectable in plasma and challenge conventional views of myocardial fuel use in HFpEF with marked diabetes and obesity and suggest substantial fuel inflexibility in this syndrome.

Unveiling air pollution patterns in Yemen: a spatial-temporal functional data analysis.

The application of spatiotemporal functional analysis techniques in environmental pollution research remains limited. As a result, this paper suggests spatiotemporal functional data clustering and visualization tools for identifying temporal dynamic patterns and spatial dependence of multiple air pollutants. The study uses concentrations of four major pollutants, named particulate matter (PM2.5), ground-level ozone (O3), carbon monoxide (CO), and sulfur oxides (SO2), measured over 37 cities in Yemen from 1980 to 2022. The proposed tools include Fourier transformation, B-spline functions, and generalized-cross validation for data smoothing, as well as static and dynamic visualization methods. Innovatively, a functional mixture model was used to capture/identify the underlying/hidden dynamic patterns of spatiotemporal air pollutants concentration. According to the results, CO levels increased 25% from 1990 to 1996, peaking in the cities of Taiz, Sana'a, and Ibb before decreasing. Also, PM2.5 pollution reached a peak in 2018, increasing 30% with severe concentrations in Hodeidah, Marib, and Mocha. Moreover, O3 pollution fluctuated with peaks in 2014-2015, 2% increase and pollution rate of 265 Dobson. Besides, SO2 pollution rose from 1997 to 2010, reaching a peak before stabilizing. Thus, these findings provide insights into the structure of the spatiotemporal air pollutants cycle and can assist policymakers in identifying sources and suggesting measures to reduce them. As a result, the study's findings are promising and may guide future research on predicting multivariate air pollution statistics over the analyzed area.

In vacuo cluster tool for studying reaction mechanisms in atomic layer deposition and atomic layer etching processes

In this paper, we introduce a vacuum cluster tool designed specifically for studying reaction mechanisms in atomic layer deposition (ALD) and atomic layer etching (ALE) processes. In the tool, a commercial flow-type ALD reactor is in vacuo connected to a set of UHV chambers so that versatile surface characterization is possible without breaking the vacuum environment. This way the surface composition and reaction intermediates formed during the precursor or etchant pulses can be studied in very close to true ALD and ALE processing conditions. Measurements done at each step of the deposition or etching cycle add important insights about the overall reaction mechanisms. Herein, we describe the tool and its working principles in detail and verify the equipment by presenting results on the well-known trimethyl aluminum–water process for depositing Al2O3.

Finite-dimensional Discrete Random Structures and Bayesian Clustering

Discrete random probability measures stand out as effective tools for Bayesian clustering. The investigation in the area has been very lively, with a strong emphasis on nonparametric procedures based on either the Dirichlet process or on more flexible generalizations, such as the normalized random measures with independent increments (NRMI). The literature on finite-dimensional discrete priors is much more limited and mostly confined to the standard Dirichlet-multinomial model. While such a specification may be attractive due to conjugacy, it suffers from considerable limitations when it comes to addressing clustering problems. In order to overcome these, we introduce a novel class of priors that arise as the hierarchical compositions of finite-dimensional random discrete structures. Despite the analytical hurdles such a construction entails, we are able to characterize the induced random partition and determine explicit expressions of the associated urn scheme and of the posterior distribution. A detailed comparison with (infinite-dimensional) NRMIs is also provided: indeed, informative bounds for the discrepancy between the partition laws are obtained. Finally, the performance of our proposal over existing methods is assessed on a real application where we study a publicly available dataset from the Italian education system comprising the scores of a mandatory nationwide test.

Influence of Dynamic Accuracy Constraints of Manipulator of Wafer Transmission Robot on Scheduling and Control of Single-Armed Cluster Tools

The wafer transfer robot is a key part of integrated circuit equipment which performs the transit of wafers precisely, quickly and steadily. The dynamic accuracy of the manipulator of the wafer transfer robot directly affects the quality of transferring and processing wafers and even the scheduling and control of the cluster tools. Thus, it is essential to study the influence of the dynamic accuracy of the manipulator of wafer transmission robots on the scheduling and control of cluster tools. In this paper, single-arm cluster tools are taken as the research object. The horizontal torsional vibration equations of the manipulator of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> robot are constructed, and the torsional vibration attenuation characteristics of the manipulator are analyzed. Based on the torsional vibration equations, the intrinsic relationships between the dynamic accuracy of the manipulator and the waiting times of the manipulator are explored when the manipulator loads and unloads the wafers. Then the two-stage approach is proposed for the scheduling and control of single-arm cluster tools. The first stage determines the minimum waiting times of the manipulator according to the intrinsic relationships between the dynamic accuracy of the manipulator and the waiting times of the manipulator when the manipulator is waiting for loading and unloading wafers in each processing module and load lock. The second stage achieves the scheduling optimization and control of single-arm cluster tools with dynamic accuracy constraints and wafer residency time constraints by establishing a mathematical programming model for the scheduling and control of single-arm cluster tools. Finally, illustrative examples are presented to analyze the influence of the dynamic accuracy of the manipulator on the scheduling and control of single-arm cluster tools.

An effective scheduling method to single-arm cluster tools for processing multiple wafer types

&lt;p style='text-indent:20px;'&gt;Recently, in semiconductor manufacturing, cluster tools have to process multiple wafer types concurrently due to product customization. Different wafer types may have different processing routes, which causes deadlocks and complicates the scheduling problem of cluster tools. This work aims to develop a general method to resolve the scheduling problem of single-arm cluster tools with a general mix of wafer types. To this end, a generic Petri net model controlled by self-loops is developed to optimally avoid deadlocks. Based on the Petri net model, an earliest starting strategy is adopted to operate single-arm cluster tools once wafers enter the tools. In order to maximize the productivity, a particle swarm optimization algorithm is constructed to determine the releasing sequence of raw wafers. Numerical examples are provided to validate the effectiveness and efficiency of the proposed method.&lt;/p&gt;

Նորարարությունը որպես տնտեսության մրցունակության բարձրացման շարժիչ ուժ / Innovation as a Driving Force for Enhancing Economic Competitiveness

Under the conditions of technological progress, the most significant prerequisite for the competitiveness of the economy in the long term and the best way to resist global challenges is the development of the innovation sector. The relationships between quantitative and qualitative characteristics of innovation and economic status index also provide evidence for that point of view. From these perspectives, it is coming on the agenda to develop and implement innovation development policy. It requires a quantitative assessment of the innovation system, one of the most important components of which is the classification of countries with different levels of development. In the paper, a similar analysis was implemented using the clustering tool, which enables to group countries according to innovation indicators and economic status index, and assess the progress or regression of countries. Based on the results, three groups of countries were distinguished: "leaders", "initiators" and "laggards". The most competitive countries were involved in the "leaders" cluster, which stand out with the best values of the indicators characterizing the innovative system. Meanwhile, the other two clusters include economically vulnerable countries with underdeveloped innovative system that require the development and implementation of effective innovation policy. From the perspective of practical significance, the results of the research are essential in the context of reviewing, adjusting policy priorities, selecting implementation mechanisms and achieving targets.

ПРОБЛЕМЫ ФИНАНСОВОГО ОБЕСПЕЧЕНИЯ ДЕЯТЕЛЬНОСТИ ПРОМЫШЛЕННЫХ КЛАСТЕРНЫХ СТРУКТУР В РОССИИ

The purpose of the study is to consider the financial support of cluster associations of organizations in order to identify existing problems and develop new approaches to the implementation of financial support for cluster structures in Russia based on cascade financing. The subject of the study is the organizational and economic relations that develop in the process of formation and use of financial resources by business entities that are part of the cluster and integrated education as a whole in the implementation of joint cluster projects by its participants. The methodological basis of the study is the works of domestic and foreign scientists devoted to the functioning of cluster structures, tools and types of their support, as well as financing problems. The research methods are methods of theoretical knowledge in terms of considering the sources of financial support for clusters, generalization and systematization of forms of support for integrated entities, analysis of the current state of industrial clusters in Russia and existing measures to support them, questioning on the problems of developing clusters in the regions, modeling in the context of the procedure for organizing financial support joint projects of industrial clusters based on cascade financing. Results of the study: the main parameters for the development of clustering processes in the industrial sector of the Russian economy are determined; the composition of the sources of financing the activities of cluster formations is systematized, while the features and possibilities of using alternative sources of financing of cluster structures are identified; a grouping of forms of support for clusters is presented, the main barriers in the development of these associations are identified; directions for the development of financial support measures for the functioning of cluster associations of organizations on the basis of cascade financing are proposed.