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This study developed Plasma Information-based Virtual Metrology (PI-VM) to predict etching process results and analyze process phenomena. Using a dual-frequency capacitively coupled plasma (CCP) etcher with C4F8/Ar/O2 plasma, we etched low aspect ratio (AR) trench patterns in amorphous carbon layer (ACL) hard masks and $\rm SiO_{2}$ molds, and developed the PI-VM statistically by integrating plasma information (PI) variables that reflect domain knowledge. The passivation effect of fluorocarbon plasma was analyzed by varying the gas ratios and the effect of ion energy was analyzed by changing the low frequency (LF) power. In the PI-VM results, the density ratios of the passivation precursor $\rm CF_{2}$ to the etchant F and O were selected as key factors for predicting the process. The selection of radical density ratios as features confirmed the dominance of plasma chemistry in low AR etching. Demonstrating high predictive accuracy with minimal data, PI-VM offers significant potential to enhance the development of semiconductor process recipes. [ABSTRACT FROM AUTHOR]

Semiconductor manufacturing processing can be jeopardized due to process fluctuations, and the degradation of equipment parts can significantly influence process variation. Timely diagnosing equipment faults causing process variations is desired in current high-end product manufacturing. This paper proposes a diagnostic method for the SiH4 gas flow rate drift using N2 vibrational transition in oxide deposition. In this research, optical emission spectroscopy (OES) and quadrupole mass spectrometer (QMS) are employed as condition monitoring sensors serving as a reference model to compare the diagnostic performance for gas flow rate drift. The study observes that the OES model exhibits much higher performance for minor diagnoses of less than 5% drift. The diagnostic model performance can be enhanced by incorporating plasma condition and gas indicators compared to when these indicators are used individually. This suggests that when conducting diagnostics for equipment and processes, it is crucial to consider indirect indicators like plasma indicators along with direct indicators such as gas radical density. The comprehensive use of both types of indicators enhances the diagnostic performance, providing a more accurate assessment of the conditions and potential problem in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

In wafer etching, regular cleaning and maintenance of process chambers are necessary to reduce particle contamination of etched wafers during the wafer transfer process. Investigating alternative cleaning and maintenance is imperative. This study analyzed the number of particles falling onto a silicon wafer when the pressure difference within the process chamber was manipulated. We observed that rapid opening of the pressure control valve, which regulates the chamber’s pressure, caused contamination during wafer transport. This was particularly true when the change in the pressure ratio was considerable. The by-products near the side of the chamber’s pressure control valve were activated and transported. We verified this finding by adjusting the opening ratio of the pressure control valve (i.e., its degree of opening). We proposed that during the transition step of the etching process, this opening ratio can be controlled by regulating the process pressure through gas flow settings. This method could suppress the deposition of reflected particles originating from the turbomolecular pump’s pumping line on wafers, thereby minimizing the contamination of wafers. [ABSTRACT FROM AUTHOR]

For single crystal diamond (SCD) to gain practical use in technical applications including solid state electronics, thin (< $1 ~\mu \text{m}$), doped epitaxial SCD layers with very low (<1 nm) surface roughness are required. Conventional SCD chemical mechanical polishing (CMP) processes are insufficient to meet the requirements of such applications because the material removal rate (MRR) is often characterized inappropriately, and the material removal uniformity is seldom considered. In this study, chemical vapor deposition (CVD) growth of two lightly boron doped (p-) epilayers was performed on 3° misoriented (100) high pressure high temperature substrates of 4.5 x $4.5 mm^{2}$ area. A subsequent 8-hour oxidative CMP process utilizing potassium permanganate and a novel self-leveling holder design decreased the average surface roughness from 3.83 nm and 1.57 nm to 0.20 nm and 0.16 nm for the two samples, respectively. MRRs were determined by evaluating five circular wear monitor structures in each sample by atomic force microscopy before and after the CMP process. The average MRRs were found to be 38.6 nm/hr and 37.3 nm/hr for the two samples. The purpose of this study is to demonstrate a CMP process suitable for polishing thin SCD epilayers to meet the needs of solid-state electronics applications. [ABSTRACT FROM AUTHOR]

Semiconductor industry is experiencing a rising demand for environmentally friendly processes with the emphasis on green policies and worldwide environmental sustainability. Nitrogen trifluoride (NF3), the most common plasma chamber cleaning agent gas, poses a significant concern as a potent greenhouse gas since it has global warming potential (GWP), 740 times and 6 times higher than that CO2 and N2O. This study investigated the exhaust gas using quadrupole mass spectroscopy (QMS) and analyzed the change in cleaning speed and the type of exhaust gas through plasma monitoring using optical mass spectroscopy (OES). The objective is to lower the use of the amount of NF3 gas in chamber cleaning process to partially contribute the environmental sustainability in the point of semiconductor manufacturing. When a small amount of N2 was added to NF3 whose ratio of 7:23, the cleaning efficiency reached to 90% compared to NF3 gas alone. Addition of N2 positively affected electron density and temperature to increase the F-radical in remote plasma system. In conclusion, 18% of NF3 usage amount was reduced during the Sio2 deposition chamber cleaning process. [ABSTRACT FROM AUTHOR]

This article will review recent progress in cryogenic CMOS designs for future scaled quantum computing applications. After introducing the scaling challenges associated with qubit control and readout electronics operating at room temperature, approaches taken to date to cryogenic control electronics design will be discussed, focusing on the most recent relevant publications. Elements of ultra-low power circuit and system design approaches for cryogenic controllers in scaled CMOS nodes (40nm to 14nm) will be reviewed, including a discussion of current state-of-the art cryogenic controller performance and power efficiency. Note that leading designs, when operated as transmon qubit state controllers, have achieved gate error rates in the range of 10-4 to 10-3 achieving spurious free dynamic range (SFDR) of ~40dB while consuming 4-23mW of power per qubit under active control, with power efficiency strongly driven by the complexity of the digital processor integrated in the controller design. These demonstrations, while significant, are just the first steps toward achieving the performance, efficiency, and scalability that will be required for future systems. This review article will discuss fundamental tradeoffs in CMOS cryogenic designs in order to address the needs of future scaled quantum computing systems. [ABSTRACT FROM AUTHOR]

A novel inverted decoupling internal model control (IMC) design is studied in this paper. The traditional inverted decoupling method is extended to multi-dimensional complex systems with multiple time-delays and right half plane (RHP) zeros, in which the necessary condition for the existence of the inverted decoupling elements is theoretically analyzed and deduced for the first time. To guarantee the realizability of the decoupling matrix elements, compensation and approximation methods for the relative degree, time-delays and RHP zeros are discussed. The proposed inverted decoupling method is suitable for both square and non-square multi-input multi-output (MIMO) systems with the advantages of simple calculation and more flexibility. In addition, using the IMC structure to facilitate the controller design and obtain a better control effect, the conventional low-pass IMC filter is replaced by a novel improved Butterworth filter. The characteristics of the traditional and improved Butterworth filters are compared and analyzed, which shows the improved Butterworth filter has better regulating abilities to get a trade-off between tracking performance and robustness. The proposed IMC controller also provides satisfactory performance with model mismatch and additive uncertainties. Theoretical analysis and simulation comparisons verify the validity and effectiveness of the proposed scheme.

Control of time-delay processes can be achieved by using industrial PID or implementing Internal Model based Controllers (IMC). During the last century, several methods have been proposed in the literature for tuning such controllers based on models obtained by open-loop tests. The model approximation is typically a first-order plus time-delay (FOPTD) or a delayed integration process (DIP). This paper presents a comparative analysis of popular methods used in PID, robust IMC control and Sliding Mode Control (SMC). The study includes, for each method, the analysis of the impact of model mismatch on performance, their ability to reject input and output disturbances, and their complexity. The paper provides results of numerical simulations and experiments in a cascade control loop of a pump-valve-tank system where the system is presented as two cascaded FOPTD models representing, respectively, the actuator and the tank.

This paper addresses the quality control of the photolithography process in the semiconductor industry. Overlay errors in the process seriously affect the wafer yield, and cause the wafer to be forced to rework and affect the production efficiency of the equipment. We examine the current state of its process control, develop a novel overlay predict model, and verify the prediction results. This study proposes a Global Attention Generative Adversarial Networks (GAGAN) model to precisely predict the overlay error for the feed-forward data of the front layer, which is used as the important information and process parameters for the advanced process control of the current layer. Experiment results on a semiconductor shop-floor confirms that our proposed method achieves high predictive performance while maintaining extensibility and visual quality. [ABSTRACT FROM AUTHOR]

Attaining the performance and efficiency levels required by modern applications often requires the use of application-specific accelerators. However, writing synthesizable Register-Transfer Level code for such accelerators is a complex, expensive, and time-consuming process, which is cumbersome for early architecture development phases. To tackle this issue, a pre-synthesis simulation toolchain is herein proposed that facilitates the early architectural evaluation of complex accelerators aggregated to multi-level memory hierarchies. To demonstrate its usefulness, the proposed gem5-accel is used to model a tensor accelerator based on Gemmini, showing that it can successfully anticipate the results of complex hardware accelerators executing deep Neural Networks. [ABSTRACT FROM AUTHOR]

The carry-over effect is a common phenomenon in the semiconductor manufacturing process, giving the process a dynamic nature. Dynamic models are more accurate but with a consequent increase in uncertainty. Therefore, it is very important to eliminate the uncertainty and disturbance at the same time. To this end, a run-to-run (RtR) control scheme based on discrete active disturbance rejection control (DADRC) is proposed in this work. The process recipe is calculated using the state error feedback law, relying on the extended state observer (ESO) to effectively suppress the total disturbance synthesized by model uncertainty and external disturbance. Considering that tool aging often leads to drift disturbances in semiconductor manufacturing processes, a model-assisted ESO with two extended states is designed to estimate process states and total disturbance. Then an optimal observer gain is derived to minimize the estimation error. Finally, the numerical and industrial cases provide compelling evidence of the effectiveness of the proposed control scheme in suppressing tool-aging drift disturbance and a remarkable degree of tolerance towards uncertainties in the system model’s order and parameters. [ABSTRACT FROM AUTHOR]

For the 3D NAND memory, the higher oxide/nitride (ON) stacked dielectric is preferred to enhance the storage capacity, and multi-layer dielectric requirements, such as thickness uniformity and interfacial smoothness between films, gathers more interest for the performance of 3D NAND flash memory. Unsatisfactory thickness uniformity between layers is a challenge not only for the device performance but also the following etch process steps. The thickness uniformity can get worse with a little facility degradation. The degradation of the vacuum system, such as the throttle valve position, has the potential to cause process drift. This can have an impact on the thickness repeatability of each layer in a multiple dielectric stack. To reduce the process variation in multi-layer dielectric deposition for 3D NAND fabrication, process monitoring, and equipment diagnostic study is suggested in this paper. Optical emission spectroscopy (OES) is employed for plasma process monitoring and equipment state variable identification (SVID) data are investigated to find the source of the process variation. From the comparison experiments of 5 and 30 paired oxide/nitride stack deposition, we found equipment and/or facility degradation may induce the minute process drift. Among them, we suggest the potential of process drift due to the throttle valve position. [ABSTRACT FROM AUTHOR]

This research studies the problems of stochastic dynamic scheduling in production systems with batch processes and process queue time (PQT) constraints. The production systems consist of upstream batch processing machines and downstream single processing machines. Under the PQT constraint, waiting time in the downstream queue is constrained by an upper limit and violating this constraint causes scraps of jobs. The batch process increases the probability of PQT constraint violation because a batch of work-in-processes (WIPs) will move simultaneously into the downstream queue after the service completion of batch processes and suffer from higher waiting time variance. A batch process admission control (BPAC) model is developed using Markov decision processes to minimize the sum of long-run average waiting and scrap costs. The proposed BPAC model explicitly considers uncertain factors in production systems given that uncertainties are major reasons for PQT constraint violation. These uncertain factors include job arrival, processing time, and machine breakdown/repair. To cope with these uncertain factors, the BPAC control decisions change dynamically with the real-time machine health and WIP distribution. The performance of BPAC is validated using discrete event simulation, and the simulation results confirm the significant performance improvement in a wide range of batch production environments. [ABSTRACT FROM AUTHOR]

In the semiconductor manufacturing, the manufacturing equipment is managed via the quality control (QC) in which the shape of the processed feature is checked whether it meets the specification. If the shape is out of the specification, some recipe parameters are modified so that the shape meets the specification. The calculation method of the recipe parameters depends on the know-how of the individual fab engineers, which cause difficulties in the QC. To overcome this problem, we have developed an automatic calculation method of the optimal recipe parameters with Advanced Process Control (APC) system with the stacking model method in order to solve these problems. In the development process, we have also performed comparison of two gap scoring methods. In the on-site demonstration of this system, we have further optimized the exploration method of the recipe parameters. [ABSTRACT FROM AUTHOR]

This paper discusses the optimization of an advanced process monitoring scheme with interdicting fault detection and classification (FDC) capabilities that improved the control over the process development of Tantalum Nitride thin film resistors (TaN TFR). Its implementation in a high-volume manufacturing environment resulted in a reduction of misprocessed wafers, shorter equipment downtime, higher throughput, enhanced process visibility, and yield improvement. Along with optimizing FDC capabilities, implementing a short loop sampling plan reduced the turnaround time for early electrical characterization by a factor of four; this allowed for timely inline adjustments within the fabrication process to tighten the statistical process control (SPC) over the distribution of the process control monitor (PCM) TaN resistor value and improve thin film uniformity. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are one important enabler for the evolution of sustainable transportation sector. However, one major barrier to the widespread adoption of EVs is their limited battery capacity and the excessive time required to fully charge depleted batteries. Although techniques such as charging with a rapid charger and battery swapping may partially alleviate such concerns, the corresponding high capital investment may not be profitable for areas with low EV adoption rates. Moreover, the diversified charging demands of randomly distributed EVs, both spatially and temporally, restrict high-quality services from current fixed charging stations (FCSs). To resolve such challenges, mobile charging services (MCSs) have been investigated as a supplemental charging method for EVs, wherein energy replenishment is provided by mobile charging vehicles (MCVs). Under the framework of the Internet of EVs (IoEV), this article describes the concept of MCSs and investigates their charging merits under some distinct charging scenarios, e.g., real-time response service and emergency rescue service. A case study concerning real-time response services is investigated to illustrate the influence of MCSs on the current charging system. Simulation results demonstrate the ability of MCSs to improve the quality of charging service by reducing the average service time and service acceptance ratio. Finally, technical challenges, future research issues, and possible methodologies regarding the MCSs for EVs are discussed. [ABSTRACT FROM AUTHOR]

3D wafer packaging represents a significant component of the total wafer level processing cost. Replacement of the Chemical Mechanical Polishing (CMP) process step with a corresponding dry etch can yield significant time and cost savings. Incorporating equipment already utilized in the 3D integrated wafer packaging process during the subsequent Through Silicon Via (TSV) reveal step, process efficiencies can be achieved, with overall die yields being maintained. Using dry etch technology to treat a 200nm rough back-ground silicon surface, a smooth surface with a peak to valley roughness of less than 6nm is demonstrated. This patented process differs from other dry etch smoothing techniques in that it aims to eliminate any visual grind marks rather than just reducing the surface roughness. The elimination of visible grind marks is critical in later optical inspection where they are falsely identified as defects. The quality of the surface is equivalent to that of a CMP processed wafer and as such, this process has been implemented in manufacturing replacing the CMP step. The novel process described combines a surface modification followed by a roughness reduction in an iterative manner to produce a smooth surface without visible grind marks post processing. [ABSTRACT FROM AUTHOR]

Microgrids offer superiorities such as reducing energy costs and increasing the quality of energy, with the use of renewable energy sources and the effective use of energy storage unit created with innovative batteries. Furthermore, this structure, which helps to reduce the carbon footprint, will become undeniably critical to use in near future with the nanogrid and smart grid. As another development, an artificial intelligence (AI)-based control infrastructure brought to us by machine learning stands out as more beneficial than classical control methods. With this framework, which is called reinforcement learning (RL), it is promised that the system to be controlled can be more efficient. At this point, the thrifty use of energy storage unit, which is the most important tool that will increase the profitability of microgrids and enhance the proficiency of energy use, is associated with an RL-based energy control system. While this study focuses on an AI-based control infrastructure, it proposes a method utilizing an RL agent trained with a novel environmental model proposed specifically for the energy storage unit of microgrids. The advantages of this method demonstrated with the results are obtained, are shown and examined. [ABSTRACT FROM AUTHOR]

Wire and arc additive manufacturing (WAAM) is a promising method for directly manufacturing parts with complex shapes. However, the accuracy of the existing welding parameter planning methods would dramatically decrease when the bead geometry changes dynamically due to the long-term dependence, strong coupling, and hysteresis properties of the WAAM process. To this end, a nonautoregressive dynamic model is proposed to predict the bead geometry, and an adaptive model predictive control (aMPC) method is proposed to plan welding parameters to achieve high manufacturing accuracy. First, in the proposed dynamic model, the long-term dynamic characteristics of the WAAM process are modeled by a resample long short-term memory network by considering the fluidity of the welding pool, which is the crucial factor of dynamic characteristics of the welding process. Second, in the proposed aMPC method, the strong coupling is addressed by high multiobjective performance, and the hysteresis is considered by a long control horizon. Thus, the aMPC method can reduce the control latency and improve the manufacturing accuracy for varying geometry beads. The proposed methods are validated by experiments, which indicate that the proposed methods are effective. [ABSTRACT FROM AUTHOR]

Exponentially weighted moving average (EWMA) controllers have been extensively studied for run-to-run (RtR) control in semiconductor manufacturing processes. However, the EWMA controller with a fixed weight struggles to achieve excellent performance under unknown stochastic disturbances. To improve the performance of EMWA via online parameter tuning, an intelligent strategy using deep reinforcement learning (DRL) technique is developed in this work. To begin with, the weight adjusting problem is established as a Markov decision process. Meanwhile, simple state space, action space and reward function are designed. Then, the classical deep deterministic policy gradient (DDPG) algorithm is utilized to adjust the weight online. Moreover, a quantile regression-based DDPG (QR-DDPG) algorithm is further verified the effectiveness of the proposed method. Finally, the developed scheme is implemented on a deep reactive ion etching process. Comparisons are conducted to show the superiority of the presented approach in terms of disturbance rejection and target tracking. [ABSTRACT FROM AUTHOR]

Nonstationary characteristics and lack of time delays analysis remain two large obstacles to diagnosing fault root cause in industrial processes. However, ignoring these two issues, many traditional methods tend to introduce some spurious or indirect causal relationships that interfere with root cause judgments. In this article, multiscale partial symbolic transfer entropy (MPSTE) is proposed to handle the above-mentioned problems. On the one hand, a multivariate multiscale filter module of MPSTE is designed to estimate the information transfer on multiple time scales and detect time delays while considering the indirect causality of the multivariate industrial system. On the other hand, to capture the causal information from the nonstationary processes, MPSTE introduces a segmental symbolization procedure and maps time series into finite rank vectors, overcoming the limitation of stationarity. Moreover, two causal criteria for MPSTE are constructed to identify the direction and significance level of causality correspondingly. MPSTE can thus provide sufficient information for causal relationships and exclude spurious causality. Finally, a causal diagram construction strategy with time delay based on MPSTE is established to diagnose the root cause and trace the fault propagation path. The results of experimental verification in a coal mill rig prove the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This article proposes a multiport control method to enable partial power processing (PPP) in a medium-voltage (MV) multiport solid-state transformer (SST). MV multiport SSTs are promising in integrating low-voltage dc sources or loads such as solar photovoltaic, energy storage, and electric vehicles into smart grids without bulky line-frequency transformers. Compared with voltage-source SST, current-source (CS) SST features single-stage isolated bidirectional ac/ac, ac/dc, or dc/dc conversion using an inductive dc link. For a multiport CS SST, it is revealed in this article that the PPP capability can be enabled through the proposed control without extra hardware, different from the case of voltage-source converters where special hardware architecture is required for the PPP. With the PPP, most power exchange between LV ports is processed by only a fraction of the entire conversion stage, leading to reduced dc-link current, volume, loss, and improved efficiency. The proposed multiport PPP control scheme is analyzed to verify the advantages across a wide voltage and power range against conventional full power processing (FPP) multiport control, using the soft-switching SST (S4T) with reduced conduction loss as an example. Comparative experimental results based on a SiC three-port S4T prototype verify the effectiveness of the proposed PPP scheme against the FPP scheme under both steady state and dynamic conditions. The dc-link current reduction is measured to be more than 36%. Significantly, the proposed multiport PPP control scheme is generic and applicable to any hard-switching or soft-switching CS SSTs without extra hardware. [ABSTRACT FROM AUTHOR]

Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations, based on network prior knowledge, induce different search behaviors with similar characteristics to various perspectives. Specifically, it is difficult for users to determine a suitable transformation a priori. This article proposes a multi-transformation evolutionary framework for influence maximization (MTEFIM) with convergence guarantees to exploit the potential similarities and unique advantages of alternate transformations and to avoid users manually determining the most suitable one. In MTEFIM, multiple transformations are optimized simultaneously as multiple tasks. Each transformation is assigned an evolutionary solver. Three major components of MTEFIM are conducted via: 1) estimating the potential relationship across transformations based on the degree of overlap across individuals of different populations, 2) transferring individuals across populations adaptively according to the inter-transformation relationship, and 3) selecting the final output seed set containing all the transformation's knowledge. The effectiveness of MTEFIM is validated on both benchmarks and real-world social networks. The experimental results show that MTEFIM can efficiently utilize the potentially transferable knowledge across multiple transformations to achieve highly competitive performance compared to several popular IM-specific methods. The implementation of MTEFIM can be accessed through the external link in the footnote. [ABSTRACT FROM AUTHOR]

Business models (BMs) help firms to translate abstract strategic decisions into their daily operations. Because BMs mediate between strategy and operations, BM innovation involves both high-level strategic experiments and low-level implementation into operations and technologies. However, most existing empirical studies regarding BM innovation focus exclusively on strategic management and marketing theory. This article examines whether the performance implications of BM experimentation are mediated by the time and effort spent on implementing BMs into operating models and enterprise architectures. We adopt an empirical approach, by analyzing the results of a large-scale, representative survey among European small and medium-sized enterprises (SMEs). In line with the existing literature, the research confirms that spending time and effort experimenting with new BMs has a positive impact on the firm performance. An important new finding is that impact is in part mediated by BM implementation (i.e., translating new BMs into operating models and enterprise architectures). This article provides empirical support for the argument that BM innovation is not just a matter of strategic thinking and experimenting with BM components and BM architecture, but that is also involve aligning BMs to operations and enterprise architectures. Our findings call for future research into the process of BM innovation from a business modeling, an enterprise architecting and engineering management perspective, with a focus on information exchange, business processes, and supporting IT applications and infrastructure. [ABSTRACT FROM AUTHOR]

Blockchain can be used to build a peer-to-peer, secure, and smart transaction system. As a horizontal technology that has changed several fields of industry, blockchain has tremendous potential to transform healthcare systems as well. In this article, a systematic review is conducted to critically evaluate 64 articles on blockchain-based healthcare systems, published between 2016 and January 2020 in 21 conferences, 33 journals, and ten online sources. The aim of this article is to answer three main questions. First, what are the applications of blockchain in the healthcare systems, and what are the structures and challenges of applying blockchain to a specific healthcare domain? Second, what are the technical, temporal, and spatial aspects of the currently developed blockchain applications for different healthcare domain? Third, what are the future research directions in designing and implementing blockchain-based healthcare systems? Statistical facts about the technical aspects of these 64 articles show that most of the proposed blockchain-based healthcare systems use private blockchain and Ethereum platforms; furthermore, the majority of the authors are affiliated with research institutions in the USA and China. We also discuss potential future research directions, e.g., integrating the blockchain in artificial intelligence based solutions, cloud-computing-based solutions, and parallel blockchain architecture. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPSs) have attracted increasing attention in recent years due to their promise for substantial and long-term benefits to society, economy, environment, and citizens. In addition, the rapid advances in computing, communication, and storage technologies have resulted in a revolution in the information communication technology domain and domination in the industry context. The utilization of CPSs in industrial settings has led to industrial cyber-physical systems (ICPSs), which, in conjunction with the information-driven interactions, enables large-scale cooperation in industrial facilities and among all the stakeholders of the value chain. Hence, the research on ICPSs is essential, especially with respect to the engineering of such systems for industrial applications. This article presents an overview of recent developments in ICPSs. We first introduce the architecture of ICPSs. Then, we review the developments of ICPSs in relevant research domains. Finally, this article concludes by presenting some potential future research directions on ICPSs. [ABSTRACT FROM AUTHOR]

Thanks to the booming development of artificial intelligence, 5G technology, and intelligent manufacturing technology, numerous intelligent edge devices contained in the industrial Internet of Things (IIoT) are endowed with the ability to mine knowledge from perceived massive data. Knowledge-driven IIoT plays an unprecedented role in application fields such as cyber-physical systems and Industry 4.0. However, knowledge is generally scattered across the distributed edge devices of IIoT. Therefore, in order to further achieve the edge intelligence in IIoT, it is very important to explore an efficient edge knowledge sharing method. In this article, we establish a decentralized knowledge sharing platform in IIoT. First, for public knowledge, a dynamics model that can quantitatively describe its sharing process is established by using the system dynamics theory. Furthermore, a control method for maximizing public knowledge sharing under constraints based on the optimal control theory is presented. Second, for private knowledge, a trusted transaction control method based on blockchain technology is proposed. By developing both smart contract and lightweight consensus mechanism, the efficient peer-to-peer sharing of private knowledge is realized, and the integrity of knowledge and the privacy of participants are protected. The results of extensive experiments show that the proposed method can eliminate the obstacles of knowledge sharing among edge devices in IIoT, and further promote the development of edge intelligence empowered 5G-enabled IIoT applications. [ABSTRACT FROM AUTHOR]

The inverse calculation of burden distribution matrix (BDM) is one of the most important challenges in the blast furnace operation in iron-making processes. In general, blast furnace consumes 65% of the total energy for the whole steel-making. Focusing on this practical challenge, this article proposes a new burden distribution spatial model in calculating burden charging process, and develops a B-spline approximation-based probability density function (PDF) control algorithm to assign the expected thickness distribution of burden layer and, thus, develops a new method for the required inverse calculation of BDM. First, a novel method for the thickness distribution of burden layer is given using B-spline model to produce an expected distribution shape subjected to a desired tracking within a specific spatial constraint. Then, according to the coexistence of continuous and bounded discrete variables in BDM, a novel hybrid optimization control method by combining integer programming and PDF tracking is further established for the effective inverse calculation of BDM. Finally, the proposed PDF-based iterative inverse calculation of BDM using B-spline models are tested using various data from industrial examples. The simulation results show that the proposed method is well suited to solve the BDM inverse calculation problem in practice. [ABSTRACT FROM AUTHOR]

As the controller parameter adjustment process is simplified significantly by using learning algorithms, the studies about learning-based control attract a lot of interest in recent years. This article focuses on the intelligent servo control problem using learning from desired demonstrations. Compared with the previous studies about the learning-based servo control, a control policy using the broad learning system (BLS) is developed and first applied to a microrobotic system, since the advantages of the BLS, such as simple structure and no-requirement for retraining when new demos’ data is provided. Then, the Lyapunov theory is skillfully combined with the complex learning algorithm to derive the controller parameters’ constraints. Thus, the final control policy not only can obtain the movement skills of the desired demonstrations but also have the strong ability of generalization and error convergence. Finally, simulation and experimental examples verify the effectiveness of the proposed strategy using MATLAB and a microswimmer trajectory tracking system. [ABSTRACT FROM AUTHOR]

The new generation of the industrial cyber-physical system (ICPS) supported by the edge computing technology facilitates the deep integration of sensing and control. System observability is the key factor to characterize the internal relationship of them. In most existing works, the observability is regarded as the assumption for subsequent sensing and control. But, in fact, with the gradually expanded network scale, this assumption is more difficult to directly satisfy sensing design. For this problem, we propose the observability guaranteed method (OGM) for edge sensing and control co-design. Specifically, the nonconvex observability condition is transformed into the convex range of key parameters of the sensing strategy based on the graph signal processing (GSP) technology. Then, we establish the relationship between these parameters and control performance. In OGM, except the previous design from sensing to control, we reversely adjust the sensing design for control demands to satisfy observability. Finally, our algorithm is applied into the hot rolling laminar cooling process based on the semiphysical evaluation. The effectiveness is verified by the results. [ABSTRACT FROM AUTHOR]

Recently, data-driven fault diagnosis techniques, especially multivariate statistical process monitoring methods, have been extensively investigated and widely applied to industries. Whether the monitored system is under closed- or open-loop control has a strong impact on the development of fault diagnosis strategies, but this point has not been taken seriously into consideration. This work aims to provide an effective data-driven method for sensor fault diagnosis under closed-loop control with modified slow feature analysis (SFA). The SFA is first revisited and compared with the well-known principal component analysis approach. Through theoretical analysis and an intuitive example, the influence of feedback control on sensor fault diagnosis is demonstrated, and to achieve successful detection, a strategy of incorporating more variables for monitoring is suggested. Then, based on SFA, a new sensor fault detection and classification method is developed. Its detectability analysis is carried out, based on which improved methods are proposed for efficient detection of incipient sensor faults. Finally, case studies on the continuous stirred tank reactor benchmark process demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This work presents an automated design methodology for time-mode proportional and integral (PI) controllers aimed for an on-chip switched-capacitor (SC) dc/dc converter system. The basis of this design is the use of evolutionary optimization algorithms to find the near-optimal set of sizings for the time-mode PI controller. It is motivated due to the difficulty faced when tuning the controller parameters at a circuit level, which arise as a result of the presence of modeling inaccuracies and the small region for the linearized model where it is defined. Moreover, this design proposes the required modifications for the original design presented for the inductor-based dc/dc converter. These modifications are necessary to operate the SC dc/dc converter in slow switching limit (SSL). The addition of a pulse-width-modulated (PWM)-to-pulse frequency-modulated (PFM) conversion block is presented and elaborated in this article. The controller is codesigned using the differential evolution algorithm for the circuit level implementation to mitigate the issues prior mentioned. The optimized controller is then tested in a simulation environment using TSMC $0.18~\mu \text{m}$ technology. The results of the optimized controller were superior to those of a conventional controller. The optimized system achieved an overall efficiency of 79.1%. [ABSTRACT FROM AUTHOR]

This article is concerned with the stochastic stability and $l_{1}$ -gain control of two-dimensional (2-D) positive Markov jump systems (PMJSs) with directional delays based on the Roesser model. First, necessary and sufficient conditions (NSCs) for the stochastic stability of the addressed system are established by constructing a deterministic “equivalent” system and applying a stochastic copositive Lyapunov function. This reveals that the stochastic stability of 2-D PMJSs with delays is affected by the size of directional delays, the transition matrix, and system matrices. Second, the exact $l_{1}$ -gain index is calculated and NSCs in the form of linear programming (LP) are established for the addressed system. Systematic methods for the $l_{1}$ -gain controller design are proposed so that the closed-loop system (CLS) is positive and stochastically stable and has an optimal $l_{1}$ -gain performance, which is achieved using an iterative algorithm and an analytical calculation method for a single-input case. Finally, the potency and accuracy of the theoretical results are verified using two examples. [ABSTRACT FROM AUTHOR]

Organizational innovation capability is a critical competitive strategy to generate and execute ingenious ideas necessary to offer new services, processes, and products to stay relevant and competitive. This becomes important in the context of small and medium-sized enterprises (SMEs) who depend significantly on stakeholders for an uninterrupted supply of relevant resources to produce and provide offerings to the markets amidst fierce market competition to stay competitive. We draw upon resource dependence and dynamic capabilities theory to investigate how stakeholder pressure acts upon SMEs to utilize their absorptive capacity of developing innovation capability to improve their overall performance. We collected data from 291 SMEs from the manufacturing sector to test the hypotheses of the study. Results suggest that absorptive capacity mediates the influence of stakeholder pressure on innovation capability. Furthermore, innovation capability too mediates the relationship between absorptive capacity and firm performance. This article contributes to theoretical and practical implications. [ABSTRACT FROM AUTHOR]

In this article, we explore the topic of supply chain resilience (SCR) in an open-system context. Systems theory and cybernetics are important methodologies in the analysis of complex networks. However, relatively little is known about their application in the supply chain (SC) operations. Using systems theory and cybernetics for SCR conceptualization, we argue that a systems-theoretical lens can help to extend the state-of-the-art research to describe SCR based on the concept of balancing vulnerability and recoverability over time. Toward this end, we define and test a new resilience measure for SCR using the Bayesian network approach with a compounding function of vulnerability and recoverability. We further propose managerial actions for implementing an open-system view of SCR. To demonstrate the applicability and usefulness of our method, we measure and analyze the resilience levels of a manufacturer. Our results provide useful decision-making support and can be valuable for SC managers seeking to balance risk mitigation and recovery capabilities. The framework and measure developed can also be used to guide firms seeking to analyze the impact of disruptions on SCs through the consideration of both performance degradation and recovery. [ABSTRACT FROM AUTHOR]

This article investigates the consensus problem of mixed-order nonlinear multiagent systems (MASs). First, a new research framework of consensus control for MASs with hybrid-order dynamics is established. In this framework, the order of low-order dynamic subsystems is increased to higher-order dynamic subsystems by means of increasing order technology, so that the mixed-order MASs can be changed into the same-order MASs. Thus, the distributed controller of hybrid-order MASs can be designed by using the consensus control method of the same-order MASs. Second, through a case study of a stochastic mixed first- and second-order nonlinear MASs, this article further expounds the design idea of the framework structure and gives the concrete design form of the distributed controller and the stability analysis of the closed-loop system. Finally, simulations are given to verify the effectiveness of the distributed control protocol in this case. [ABSTRACT FROM AUTHOR]

This article aims to stabilize probabilistic Boolean networks (PBNs) via a novel pinning control strategy. In a PBN, the state evolution of each gene switches among a collection of candidate Boolean functions with preassigned probability distributions, which govern the activation frequency of each Boolean function. Due to the existence of stochasticity, the mode-independent pinning controller might be disabled. Thus, both mode-independent and mode-dependent pinning controller are required here. Moreover, a criterion is derived to determine whether mode-independent controllers are applicable while the pinned nodes are given. It is worth pointing out that this pinning control is based on the $n\times n$ network structure rather than $2^{n} \times 2^{n}$ state transition matrix. Therefore, compared with the existing results, this pinning control strategy is more practicable and has the ability to handle large-scale networks, especially sparsely connected networks. To demonstrate the effectiveness of the designed control scheme, a PBN that describes the mammalian cell-cycle encountering a mutated phenotype is discussed as a simulation. [ABSTRACT FROM AUTHOR]

This article solves the problems of finite-time control and settling-time estimation for fractional-order quaternion-valued fuzzy neural networks (FQFNNs) with time delays. A novel fractional differential inequality is established to estimate the settling time of the addressed system, which is more general and less conservative than the existing results. In addition, owing to the noncommutativity of multiplication of quaternions, the decomposition method is usually adopted to discuss the finite-time stabilization (FTS) of quaternion-valued neural networks, which inevitably doubles the dimensionality of the system and brings a great computational burden. To avoid the aforementioned issues, some new properties of the quaternion-valued signum function are presented for exploring the FTS of FQFNNs by the direct quaternion method without any decomposition. Then, 1-norm and 2-norm control strategies are designed to stabilize the addressed system in finite time, and several sufficient criteria are derived to ensure the FTS of FQFNNs. Finally, the validity of the obtained theoretical results and the superiority of the proposed estimation method are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

In this article, a fuzzy adaptive output feedback control strategy is designed for a class of uncertain nonlinear switched system with full state constraints under arbitrary switching signal. The states of the system studied in this article are unmeasurable, so a fuzzy observer is designed to estimate the unmeasurable states. At the same time, in order to ensure that the states of the system do not violate the constraints related to the desired output and states, the log-type barrier Lyapunov function method is selected to solve this constraint problem. Finally, through Lyapunov stability theory analysis, it is found that the designed control strategy can ensure that all signals in the closed-loop system are bounded, and the states of the system do not violate their corresponding constraints. In addition, a numerical simulation verifies the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

This brief focuses on the adaptive predefined-time backstepping control for Lü chaotic systems with an unknown parameter. Different from the existing research on convergence-time control for chaotic systems, based on the backstepping technique, an improved predefined-time stable lemma for strict-feedback systems is proposed, which can achieve precise control about convergence time. Moreover, a predefined-time control scheme is designed using the proposed lemma to suppress the chaotic phenomenon and stabilize the Lü chaotic systems with predefined time. And, a novel adaptive updating law is designed to make the derivative of Lyapunov function satisfy the predefined-time stable form. The stability analysis proves that the signals in the Lü chaotic system are predefined-time stable and the convergence time can be specified by the user. Finally, a simulation example is provided to prove the effectiveness of the presented predefined-time control scheme. [ABSTRACT FROM AUTHOR]

High-gain observers are powerful tools for estimating the state of nonlinear systems. However, their design poses several challenges due to the need of dealing with phenomena such as peaking and chattering. To address these issues, we propose a differentiator operator design based on a non linear second order high-gain observer, which is suited to a class of dynamical systems. Our method includes a procedure to determine high gains in order to avoid chattering in the case of noise-free models, and cut-off frequency based gain design in the case of noisy measurements. Complementary, we suggest performing observability analyses to ensure a priori the feasibility of the estimation. The main strengths of our approach are its simplicity and robustness. We demonstrate the performance of the proposed method by applying it to two processes (chemical and biological).

This work discusses the fault detection problem for two-dimensional (2-D) positive systems (PSs) with distributed delays. First, a systematic method is proposed for the computation of exact values of $\ell _{1}$ -gain and $\ell _{-}$ index for delay-free systems, and the dimension expansion technique is used to transfer the computation of $\ell _{1}/\ell _{-}$ index for 2-D delayed PSs to one of the delay-free systems. Second, necessary and sufficient conditions are established such that the 2-D delayed PSs are asymptotically stable with a desired $\ell _{1}/\ell _{-}$ performance level (PL) $\gamma /\beta $. Third, based on the above results, some NCSs are established such that the disturbance and the fault impact on the output signal are minimized and maximized, respectively. An algorithm (iterative) is formulated for the solution of the convex optimization problem. Finally, the potency and accuracy of the developed theoretical results are exhibited by an example. [ABSTRACT FROM AUTHOR]

Semiconductor fabrication involves hundreds of process steps through various manufacturing tools. These processing steps are composed of many manufacturing and inspection steps. Inspection is an important step in the fabrication process to determine whether a process is in or out of control. Abrupt manufacturing or inspection tool excursion can lead to a serious low yield problem. Although commonality analysis is a proven tool for detecting abrupt tool excursion, it has gained only limited success in detecting manufacturing tool excursion outside of inspection tools. Compared with manufacturing tools, only a small number of lots or wafers pass through inspection tools. Therefore, it is difficult to construct a sufficient lot history log for inspection commonality analysis in contrast to that of manufacturing tools. Furthermore, inspection may stress a wafer during its own processing, therefore, the target wafer is changed sequentially or randomly. Accordingly, a lot history is apt to include missing traces, which hinders finding inspection tool excursion effectively. In this paper, we propose a comparative analysis framework for commonality analysis algorithms. Performance measures are suggested. To compare the performance of the algorithms effectively, we use a synthetically generated dataset in a simulation experiment. In addition, we apply the algorithms to a real problem that occurred in the fabrication process. Our proposed algorithm demonstrates superiority over the other commonality analysis algorithms in the experiments. [ABSTRACT FROM AUTHOR]

Defect pattern detection and classification are challenging for thin-film-transistor liquid-crystal display (TFT-LCD) manufacturing. Limitations of the existing solutions for automatic optical inspection can be traced in part to the lack of a framework within which different existing and new defect patterns can be analyzed, while integrating domain knowledge and effective technologies. This study aims to develop a framework for image-based defect classification that employs the convolution neural networks without using complex and time-consuming image-processing processes in advance. An empirical study was conducted in a leading TFT-LCD manufacturing in Taiwan for validation. The results have shown that the defect patterns can be effectively classified by the proposed convolutional neural networks that outperform the existing approaches such as Support Vector Machine and Random Forest. The developed solution is implemented to effectively support the engineers. [ABSTRACT FROM AUTHOR]

As the evolution of the Internet of Things (IoT) pushes more devices to become intelligent, infrastructure within facilities must keep pace with the overall burden on the system. As the number of intelligent nodes within systems continues to increase, methods are needed in order to preserve network bandwidth and to provide reliable data sources for various control and reporting strategies. This article serves as an overview of message queuing telemetry transport and its applicability to process industries. [ABSTRACT FROM AUTHOR]

For the first time, a single flux linkage closed-loop control scheme for contactors using fast excitation control is proposed. The integrated observer and closed-loop state observer are designed to observe the flux linkage during the making and closing processes, respectively. After the contactor finishes the making and closing processes under the initial flux linkage control, it enters a self-correcting process that involves decreasing the flux linkage step by step. Two observers are used interchangeably to ensure the accuracy of flux linkage observation in this process; once the decrease in flux linkage becomes insufficient for maintaining the contactor in the closing state, the movable core separation signal is captured in time, and the critical flux linkage is determined based on the flux linkage jump of the closed-loop state observer. Then, the fast response of the single flux linkage closed-loop control can ensure the timely application of short-time and intense excitation and prevent the continued rebound of the movable core before contactor separation. Comprehensive use of the above methods enables the development of the self-correcting control strategy of a single flux linkage closed loop for a contactor. This result is of great significance for automatic bouncing inhibition and energy-saving optimization of contactors. [ABSTRACT FROM AUTHOR]

Recently, Critical Infrastructures (CI) such as energy, power, transportation, and communication have come to be increasingly dependent on advanced information and communication technology (ICT). This change has increased the connection between the Industrial Control System (ICS) supporting the CI and the Internet, resulting in an increase in security threats and allowing a malicious attacker to manipulate and control the ICS arbitrarily. On the other hand, ICS operators are reluctant to install security systems for fear of adverse effects on normal operations due to system changes. Therefore, new research is needed to detect anomalies quickly and identify attack types while ensuring the high availability of ICS. This study proposes a host-based method to detect and identify abnormalities in an Oil Refinery’s Distributed Control System (DCS) network using DCS vendor-proprietary protocols using a proposed method based on the tree-based machine learning algorithm. The results demonstrate that the proposed method can effectively detect an abnormality with the eXtreme Gradient Boosting (XGB) classifier, with up to 99% accuracy. Taken together, the results of this study contribute to the accurate detection of abnormal events and identification of attack types on the network without disrupting the normal operation of the DCS in the Oil Refinery. [ABSTRACT FROM AUTHOR]

The ongoing digitalization enables stable control processes and smooth operations of Industrial Control Systems (ICSs). A direct consequence of the highly interconnected architecture of ICSs is the introduced cyber vulnerability and increasing cyber security threats to ICSs. Numerous researches pay attention to the security problem of ICSs. However, most current studies face two challenges. First, the interaction problem between the cyber layer and the physical layer of ICSs may result in incorrect attack response strategies. Second, ICSs are real-time systems, but existing defense decision algorithms based on game theory or reinforcement learning techniques have high computational complexity, which prevents them from making decisions quickly. In this paper, we design a new multi-attribute based method for quantifying rewards and propose a multi-attribute based Q-learning algorithm to resolve the interaction problem. In addition, to overcome the limitation of slow convergence, we develop an effective parallel Q-learning (PQL) algorithm to quickly find the optimal strategy. The experimental results show the effectiveness of the PQL algorithm. Compared with the Q-learning algorithm (QL) and the deep Q-network (DQN) algorithm, our proposed solution can reduce the average completion time by 12.5 to 37 percent. [ABSTRACT FROM AUTHOR]

A scalable, non-multiplexed cryogenic 14-nm FinFET quantum bit (qubit) state controller (QSC) for use in the semi-autonomous control of superconducting transmon qubits is reported. The QSC includes an augmented general-purpose digital processor that supports waveform generation and phase rotation operations combined with a low-power current-mode single sideband upconversion ${I}/{Q}$ mixer-based RF arbitrary waveform generator (AWG). Implemented in the 14-nm CMOS FinFET technology, the QSC generates control signals in its target 4.5–5.5-GHz-frequency range, achieving an spurious free dynamic range (SFDR) > 50 dB for a signal bandwidth of 500 MHz. With the controller operating in the 4 K stage of a cryostat and connected to a transmon qubit in the cryostat’s millikelvin stage, measured transmon $T_{1}$ and $T_{2}$ coherence times were 75.7 and 73 $\mu \text{s}$ , respectively, in each case comparable to results achieved using conventional room temperature (RT) controls. In further tests with transmons, a qubit-limited error rate of 7.76 × 10−4 per Clifford gate is achieved, again comparable to the results achieved using RT controls. The QSC’s maximum RF output power is −18 dBm, and power dissipation per qubit under active control is 23 mW. [ABSTRACT FROM AUTHOR]

This article investigates a deterministic sea wave prediction-based noncausal control scheme for the launch and recovery (L&R) from a mother ship of small rigid-hulled inflatable boats (RHIBs) for maritime rescue missions. The proposed control scheme achieves an automatic hoisting process ensuring that no collisions occur between the RHIB and mothership hull by using the cable tension force as the manipulated control input. A state-space model of the L&R system is established for the first time where the wave forces and external disturbances such as wind acting on both the mothership and the small boat are fully considered. A fast and safe recovery is ensured by a fixed-time convergent sliding-mode controller, which shortens the cable length to a target value with zero terminal velocity at a predefined time instant subject to unknown disturbances and model mismatches. Since the overall dynamics of the swing angle is underactuated, a feasibility check is proposed to avoid collisions between two vessels and overlarge angular velocities by determining a proper time instant to initiate the hoisting process. To cope with the model mismatch and the external disturbance, the constraints on the swing angle and angular velocity are tightened to ensure safety. The stability of the proposed controller is proven and details of the feasibility check are given. The fidelity of the model and the effectiveness of the proposed scheme are demonstrated in simulation where a realistic sea wave is applied. [ABSTRACT FROM AUTHOR]