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Management Suggestions on Semiconductor Manufacturing Engineering: An Operations Research and Data Science Perspective
Abstract
With advances in information and telecommunication technologies and data-enabled decision-making, smart manufacturing can be an essential component of sustainable development. In the era of the smart world, semiconductor industry is one of the few global industries that are in a growth mode to smartness, due to worldwide demand. The promising significant opportunities to reduce cost, boost productivity, and improve quality in wafer manufacturing is based on the integration or combination of simulated replicas of actual equipment, Cyber-Physical Systems (CPS) and regionalized or decentralized decision-making into a smart factory. However, this integration also presents the industry with novel unique challenges. The stream of the data from sensors, robots, and CPS can aid to make the manufacturing smart. Therefore, it would be an increased need for modeling, optimization, and simulation to the value delivery from manufacturing data. This paper aims to review the success story of smart manufacturing in semiconductor industry with the focus on data-enabled decision-making and optimization applications based on “Operations Research” (OR) and “Data Science” (DS) perspective. In addition, we will discuss future research directions and new challenges to this industry.
... The need for a comprehensive theoretical framework for dynamic mechanical analysis in material selection is clear. While DMA has demonstrated its utility in understanding the behavior of materials under dynamic loading conditions, the existing body of literature lacks a cohesive structure for applying this data to real-world material selection in highperformance engineering applications (Gómez-Tejedor, et al., 2020, Khakifirooz, et al., 2019) [20,38] . Future research should focus on developing a framework that integrates DMA data with other material properties, provides standardized guidelines for material evaluation, and incorporates predictive modeling techniques to enhance decision-making (Ezatpour, et al., 2016) [16] . ...
... The need for a comprehensive theoretical framework for dynamic mechanical analysis in material selection is clear. While DMA has demonstrated its utility in understanding the behavior of materials under dynamic loading conditions, the existing body of literature lacks a cohesive structure for applying this data to real-world material selection in highperformance engineering applications (Gómez-Tejedor, et al., 2020, Khakifirooz, et al., 2019) [20,38] . Future research should focus on developing a framework that integrates DMA data with other material properties, provides standardized guidelines for material evaluation, and incorporates predictive modeling techniques to enhance decision-making (Ezatpour, et al., 2016) [16] . ...
Dynamic Mechanical Analysis (DMA) is a powerful technique for assessing the viscoelastic properties of materials, providing critical insights into their performance under various conditions. This study develops a theoretical framework for DMA in material selection for high-performance engineering applications. The framework integrates fundamental principles of DMA with practical considerations for selecting materials that meet the demanding requirements of advanced engineering fields, including aerospace, automotive, and electronics. Theoretical aspects of DMA, such as storage modulus, loss modulus, and damping ratio, are explored in detail, illustrating how these parameters correlate with material performance, durability, and stability under dynamic loading conditions. The framework emphasizes the importance of understanding the temperature, frequency, and strain rate dependence of material behavior in predicting their suitability for specific applications. The study also examines the role of DMA in identifying phase transitions, such as glass transition and crystallization, which are crucial for assessing material performance in environments with fluctuating thermal and mechanical stresses. Furthermore, the framework incorporates data from experimental DMA studies, applying these results to real-world engineering scenarios to demonstrate the utility of DMA in material selection. The theoretical framework proposes a multi-criteria decision-making (MCDM) approach for integrating DMA results with other material properties, such as strength, toughness, and fatigue resistance, to provide a holistic material selection process. Case studies are included to showcase the practical application of the framework in the selection of polymers, composites, and metals for high-performance engineering applications. By bridging the gap between DMA theory and material selection, this study contributes to the development of more efficient, reliable, and durable materials for critical engineering applications.
... The need for a comprehensive theoretical framework for dynamic mechanical analysis in material selection is clear. While DMA has demonstrated its utility in understanding the behavior of materials under dynamic loading conditions, the existing body of literature lacks a cohesive structure for applying this data to real-world material selection in highperformance engineering applications (Gómez-Tejedor, et al., 2020, Khakifirooz, et al., 2019) [20,38] . Future research should focus on developing a framework that integrates DMA data with other material properties, provides standardized guidelines for material evaluation, and incorporates predictive modeling techniques to enhance decision-making (Ezatpour, et al., 2016) [16] . ...
... The need for a comprehensive theoretical framework for dynamic mechanical analysis in material selection is clear. While DMA has demonstrated its utility in understanding the behavior of materials under dynamic loading conditions, the existing body of literature lacks a cohesive structure for applying this data to real-world material selection in highperformance engineering applications (Gómez-Tejedor, et al., 2020, Khakifirooz, et al., 2019) [20,38] . Future research should focus on developing a framework that integrates DMA data with other material properties, provides standardized guidelines for material evaluation, and incorporates predictive modeling techniques to enhance decision-making (Ezatpour, et al., 2016) [16] . ...
Dynamic Mechanical Analysis (DMA) is a powerful technique for assessing the viscoelastic properties of materials, providing critical insights into their performance under various conditions. This study develops a theoretical framework for DMA in material selection for high-performance engineering applications. The framework integrates fundamental principles of DMA with practical considerations for selecting materials that meet the demanding requirements of advanced engineering fields, including aerospace, automotive, and electronics. Theoretical aspects of DMA, such as storage modulus, loss modulus, and damping ratio, are explored in detail, illustrating how these parameters correlate with material performance, durability, and stability under dynamic loading conditions. The framework emphasizes the importance of understanding the temperature, frequency, and strain rate dependence of material behavior in predicting their suitability for specific applications. The study also examines the role of DMA in identifying phase transitions, such as glass transition and crystallization, which are crucial for assessing material performance in environments with fluctuating thermal and mechanical stresses. Furthermore, the framework incorporates data from experimental DMA studies, applying these results to real-world engineering scenarios to demonstrate the utility of DMA in material selection. The theoretical framework proposes a multi-criteria decision-making (MCDM) approach for integrating DMA results with other material properties, such as strength, toughness, and fatigue resistance, to provide a holistic material selection process. Case studies are included to showcase the practical application of the framework in the selection of polymers, composites, and metals for high-performance engineering applications. By bridging the gap between DMA theory and material selection, this study contributes to the development of more efficient, reliable, and durable materials for critical engineering applications.
... This paper will help DMs in Industry 4.0 represent their objective function based on different GO techniques, such as Kolmogorov's superposition and DC programming, which can be solved separately. Finally, Khakifirooz, Pardalos, et al. (2018) and Khakifirooz, Chien, et al. (2018) reported that applications of nonconvex optimization in decision support system development for smart manufacturing and Industry 4.0 can be a challenging direction for future research. ...
... This paper will help DMs in Industry 4.0 represent their objective function based on different GO techniques, such as Kolmogorov's superposition and DC programming, which can be solved separately. Finally, Khakifirooz, Pardalos, et al. (2018) and Khakifirooz, Chien, et al. (2018) reported that applications of nonconvex optimization in decision support system development for smart manufacturing and Industry 4.0 can be a challenging direction for future research. ...
Non-convex optimization can be found in
several smart manufacturing systems. This paper presents
a short review on global optimization (GO) methods. We
examine decomposition techniques and classify GO
problems on the basis of objective function representation
and decomposition techniques. We then explain Kolmogorov’s
superposition and its application in GO. Finally,
we conclude the paper by exploring the importance of
objective function representation in integrated artificial
intelligence, optimization, and decision support systems in
smart manufacturing and Industry 4.0.
... Advanced alloy design techniques, including the use of powder blends or hybrid materials, can also contribute to mitigating cracking by tailoring the material's properties to the specific demands of the WAAM process (Langelandsvik, et The role of monitoring and simulation tools in mitigating cracking during WAAM has also gained significant attention in recent research. Real-time monitoring techniques, such as thermal imaging, acoustic emission, and in-situ strain measurements, can provide valuable data on the thermal behavior, stress distribution, and potential crack formation during the deposition process (Gómez-Tejedor, et al., 2020, Khakifirooz, et al., 2019). By monitoring these parameters in real-time, it is possible to detect the early onset of cracking and adjust the process parameters accordingly to prevent further damage. ...
Wire Arc Additive Manufacturing (WAAM) has emerged as a transformative technique for producing complex metallic structures, particularly utilizing superalloys for high-performance applications in aerospace, energy, and automotive sectors. However, cracking in superalloy structures during WAAM remains a critical challenge, undermining mechanical integrity and limiting industrial adoption. This study proposes a conceptual framework for mitigating cracking in superalloy structures during the WAAM process. The framework integrates material science insights, process optimization strategies, and real-time monitoring technologies to address the underlying causes of cracking. The primary focus lies on understanding the interplay between thermal gradients, residual stresses, and alloy composition in the context of WAAM. Key strategies include optimizing deposition parameters such as current, voltage, and inter-pass temperature to minimize thermal stresses. Additionally, preheating and post-heating treatments are explored to reduce cracking susceptibility by refining microstructures and alleviating residual stresses. Advanced simulation tools are incorporated to predict cracking-prone zones by modeling thermal cycles and stress distributions. Furthermore, real-time monitoring systems, utilizing infrared thermography and acoustic emission techniques, are proposed to detect and mitigate cracking during fabrication. The integration of data-driven approaches, including machine learning algorithms, enhances predictive capabilities and facilitates adaptive process control. Experimental validation is conducted using nickel-based superalloys, evaluating the effectiveness of the proposed strategies in reducing crack density and improving structural integrity. Results demonstrate that the holistic framework significantly mitigates cracking by fostering favorable thermal and mechanical conditions. The study emphasizes the importance of multidisciplinary approaches in advancing WAAM technologies for superalloys, bridging the gap between research and industrial implementation. By addressing the critical issue of cracking, the proposed framework contributes to the development of reliable, high-performance additive manufacturing processes, paving the way for broader applications in critical industries.
... Advanced alloy design techniques, including the use of powder blends or hybrid materials, can also contribute to mitigating cracking by tailoring the material's properties to the specific demands of the WAAM process (Langelandsvik, et The role of monitoring and simulation tools in mitigating cracking during WAAM has also gained significant attention in recent research. Real-time monitoring techniques, such as thermal imaging, acoustic emission, and in-situ strain measurements, can provide valuable data on the thermal behavior, stress distribution, and potential crack formation during the deposition process (Gómez-Tejedor, et al., 2020, Khakifirooz, et al., 2019). By monitoring these parameters in real-time, it is possible to detect the early onset of cracking and adjust the process parameters accordingly to prevent further damage. ...
Wire Arc Additive Manufacturing (WAAM) has emerged as a transformative technique for producing complex metallic structures, particularly utilizing superalloys for high-performance applications in aerospace, energy, and automotive sectors. However, cracking in superalloy structures during WAAM remains a critical challenge, undermining mechanical integrity and limiting industrial adoption. This study proposes a conceptual framework for mitigating cracking in superalloy structures during the WAAM process. The framework integrates material science insights, process optimization strategies, and real-time monitoring technologies to address the underlying causes of cracking. The primary focus lies on understanding the interplay between thermal gradients, residual stresses, and alloy composition in the context of WAAM. Key strategies include optimizing deposition parameters such as current, voltage, and inter-pass temperature to minimize thermal stresses. Additionally, preheating and post-heating treatments are explored to reduce cracking susceptibility by refining microstructures and alleviating residual stresses. Advanced simulation tools are incorporated to predict cracking-prone zones by modeling thermal cycles and stress distributions. Furthermore, real-time monitoring systems, utilizing infrared thermography and acoustic emission techniques, are proposed to detect and mitigate cracking during fabrication. The integration of data-driven approaches, including machine learning algorithms, enhances predictive capabilities and facilitates adaptive process control. Experimental validation is conducted using nickel-based superalloys, evaluating the effectiveness of the proposed strategies in reducing crack density and improving structural integrity. Results demonstrate that the holistic framework significantly mitigates cracking by fostering favorable thermal and mechanical conditions. The study emphasizes the importance of multidisciplinary approaches in advancing WAAM technologies for superalloys, bridging the gap between research and industrial implementation. By addressing the critical issue of cracking, the proposed framework contributes to the development of reliable, high-performance additive manufacturing processes, paving the way for broader applications in critical industries.
... Advanced alloy design techniques, including the use of powder blends or hybrid materials, can also contribute to mitigating cracking by tailoring the material's properties to the specific demands of the WAAM process (Langelandsvik, et The role of monitoring and simulation tools in mitigating cracking during WAAM has also gained significant attention in recent research. Real-time monitoring techniques, such as thermal imaging, acoustic emission, and in-situ strain measurements, can provide valuable data on the thermal behavior, stress distribution, and potential crack formation during the deposition process (Gómez-Tejedor, et al., 2020, Khakifirooz, et al., 2019). By monitoring these parameters in real-time, it is possible to detect the early onset of cracking and adjust the process parameters accordingly to prevent further damage. ...
Wire Arc Additive Manufacturing (WAAM) has emerged as a transformative technique for producing complex metallic structures, particularly utilizing superalloys for high-performance applications in aerospace, energy, and automotive sectors. However, cracking in superalloy structures during WAAM remains a critical challenge, undermining mechanical integrity and limiting industrial adoption. This study proposes a conceptual framework for mitigating cracking in superalloy structures during the WAAM process. The framework integrates material science insights, process optimization strategies, and real-time monitoring technologies to address the underlying causes of cracking. The primary focus lies on understanding the interplay between thermal gradients, residual stresses, and alloy composition in the context of WAAM. Key strategies include optimizing deposition parameters such as current, voltage, and inter-pass temperature to minimize thermal stresses. Additionally, preheating and post-heating treatments are explored to reduce cracking susceptibility by refining microstructures and alleviating residual stresses. Advanced simulation tools are incorporated to predict cracking-prone zones by modeling thermal cycles and stress distributions. Furthermore, real-time monitoring systems, utilizing infrared thermography and acoustic emission techniques, are proposed to detect and mitigate cracking during fabrication. The integration of data-driven approaches, including machine learning algorithms, enhances predictive capabilities and facilitates adaptive process control. Experimental validation is conducted using nickel-based superalloys, evaluating the effectiveness of the proposed strategies in reducing crack density and improving structural integrity. Results demonstrate that the holistic framework significantly mitigates cracking by fostering favorable thermal and mechanical conditions. The study emphasizes the importance of multidisciplinary approaches in advancing WAAM technologies for superalloys, bridging the gap between research and industrial implementation. By addressing the critical issue of cracking, the proposed framework contributes to the development of reliable, high-performance additive manufacturing processes, paving the way for broader applications in critical industries.
... The learning-based systems can be applied to many smart manufacturing and Industry 4.0 applications [46], [47] based on partially observable processes. Our proposed POMDP the method can be useful for coping with many practical problems such as dynamic decision-making for both microlevel and macrolevel problems in industry 4.0 and society 5.0 [48], [49], often, decision-makers have incomplete information about the state of the system, and consequently, this situation leads them to apply POMDPs. ...
The properties of a learning-based system are particularly relevant to the process study of the unknown behavior of a system or environment. In the semiconductor industry, there is regularly a partially observable system in which the entire state of the process is not directly or fully visible due to uncertainties or disturbances. The model for studying such a system that permits uncertainties regarding the stochastic (Markov) process for state information acquisition is called a partially observable Markov decision process (POMDP). This study aims to deal with the optimization issue of compensation control bias of a dynamic multilayer lithography process in wafer fabrication with prior information, the existence of high-dimensionality, and unmeasurable uncertainties. We show how the POMDP on a linear state-space model with uncertainties can encode the information from past runs and layers, and deal with accumulated overlay error at the current run and layer. The Gibbs sampling is applied to optimize the belief function of POMDP optimization approach.
With advances in information and telecommunication technologies and data-enabled decision making, smart manufacturing can be an essential component of sustainable development. In the era of the smart world, semiconductor industry is one of the few global industries that are in a growth mode to smartness, due to worldwide demand. The important opportunities that can boost the cost reduction of productivity and improve quality in wafer fabrication are based on the simulations of actual environment in Cyber-Physical Space and integrate them with decentralized decision-making systems. However, this integration faced the industry with novel unique challenges. The stream of the data from sensors, robots, and Cyber-Physical Space can aid to make the manufacturing smart. Therefore, it would be an increased need for modeling, optimization, and simulation for the value delivery from manufacturing data. This paper aims to review the success story of smart manufacturing in semiconductor industry with the focus on data-enabled decision making and optimization applications based on operations research and data science perspective. In addition, we will discuss future research directions and new challenges for this industry.
Shrinkage in semiconductor devices affects the process window of all wafer fabrication steps including plasma etching. Drifts or shifts are most significant effects on the etching process due to shrinkage in semiconductor devices. Any drift or shift affects on critical dimensions (CD) of the wafer and changes the thickness and the width over time. Therefore, there would be an essential need for estimation and minimization of CD variation on a wafer-to-wafer basis by optimization techniques. This study aims to design a learning-based control system for monitoring the CD in Dry-Etching process. Feedforward-feedback control technique is used to reduce CD variation. Among all learning-based control systems, the Iterative Learning Control (ILC) integrated with Virtual Meteorology (VM) data, as a well-known system which can involve both feedforward signal from the past events, and feedback signals from the output of the current event is used to learn the behavior of the system and enhance the performance of the controller run-by-run. The proposed control model is optimized by gradient learning approach. The result is validated through the simulated study manipulated from empirical data and shows the advantage of the proposed feedforward-feedback learning controller than the common run-to-run exponentially weighted moving average (EWMA) control design.
— It is vital to have an exclusive modification in semiconductor production process because of meeting differentiated customer demands in dynamic and competitive global
minuscule semiconductor technology market and the highly complex fabrication process. In this paper, we propose a control system based on the dynamic mixed-effect least-square support vector regression (LS-SVR) control system for overlay error compensation with stochastic metrology delay to minimize the misalignment of the patterning process. Moreover, for the stability of the control system in the presence of metrology delay and
to deal with nonlinearity among the overlay factors, the novel Lyapunov-based kernel function is merged with the LS-SVR controller. The proposed controller’s operation has been validated and implemented by a major semiconductor manufacturer in
Taiwan. The experiments are verified that mixed-effect LS-SVR controller has the higher validity and higher efficiency in comparison with the exponentially weighted moving average (EWMA) and threaded EWMA controllers which had been previously
implemented at the company or applied in similar studies.
Note to Practitioners—Due to high production complexity in semiconductor manufacturing process, a meticulous and intelligent process control is needed to achieve higher throughput and customer satisfaction. Monitoring a complex system is challenging because the process components and variables operate autonomously and interoperate with other manufacturing segments. This paper proposes a novel run-to-run (R2R) control system to compensate the overlay error during the photolithography process that efficiently deals with the high-mixed manufacturing environment and metrology delay.
This paper is focused on analysing the energetic key performance indicators of a hybrid electric bus fleet in order to improve its energy management (at local and fleet level) and profitability. The analysed fleet is composed of buses with parallel and series configurations and include energy storage systems based on batteries and ultra capacitors. The test routes have been selected from a data base of urban standardised cycles. In a first stage, a dynamic programming approach has been applied to determine the initial optimal operation performance for each bus route. Then, several disruptions (e.g. traffic jams, auxiliary consumption and passenger variations) have been added to the routes to simulate "real" road and daily operation conditions. In this paper, a fleet learning methodology is proposed to analyse, process and decide based on the collected data from "real" conditions of the whole fleet. This data is used for monitoring the energetic key performance factors by learning from the buses with the best energetic behaviour. Finally, a decision making process is applied to improve the local energy management of the less-efficient bus.
Big data analytics helps in analyzing a huge set of data whereas IoT is about data, devices and connectivity. Internet of Things (IoT) involves connecting physical objects to the Internet to build smart systems and universal mobile accessibility advanced technologies like Intelligent Transportation System (ITS). IoT solutions are playing a major role in driving the global IoT in Intelligent Transportation System. Communication between vehicles using IoT will be a new era of communication that leads to ITS. IoT is a combination of storing and processing sensor data and computing using data analytics to achieve and assist in managing the Traffic system effectively. IoT based Intelligent transportation system (IoT-ITS) helps in automating railways, roadways, airways and marine which enhance customer experience about the way goods are transported, tracked and delivered. A case study on Intelligent Traffic Management System based on IoT and big data, which will be a part of, smart traffic solutions for smarter cities. The ITS-IoT system itself forms an eco-system comprising of sensor systems, monitoring system and display system. There are several techniques and algorithms involved in full functioning of IoT-ITS. The proposed case study will examine and explain a complete design and implementation of a typical IoT-ITS system for a smart city scenario set on typical Indian subcontinent. This case study will also explain about several hardware and software components associated with the system. How concepts like Multiple regression analysis, Multiple discriminant analysis and logistic regression, Cojoint analysis, Cluster analysis and other big data analytics techniques will merge with IoT and help to build IoT-ITS will also be emphasized. The case study will also display some big data analytics results and how the results are utilized in smart transportation systems.
Run-to-run (R2R) control is cutting-edge technology that allows modification of a product recipe between machine “runs,” thereby minimizing process drift, shift, and variability-and with them, costs. Its effectiveness has been demonstrated in a variety of processes, such as vapor phase epitaxy, lithography, and chemical mechanical planarization. The only barrier to the semiconductor industry’s widespread adoption of this highly effective process control is a lack of understanding of the technology. Run to Run Control in Semiconductor Manufacturing overcomes that barrier by offering in-depth analyses of R2R control.