Digital Twins for Plasma Etching Parameter Optimization in Semiconductor FABs
JUN 3, 20269 MIN READ
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Digital Twin Plasma Etching Background and Objectives
Plasma etching has emerged as one of the most critical processes in semiconductor manufacturing, enabling the precise removal of material layers to create intricate circuit patterns on silicon wafers. This subtractive manufacturing technique utilizes ionized gases to selectively etch materials with nanometer-scale precision, making it indispensable for producing advanced semiconductor devices with feature sizes below 10 nanometers. The evolution from wet chemical etching to plasma-based dry etching marked a pivotal transformation in the industry, driven by the need for better anisotropic etching profiles and reduced contamination risks.
The complexity of plasma etching processes has grown exponentially with advancing technology nodes. Modern semiconductor fabrication facilities face unprecedented challenges in maintaining process stability, yield optimization, and defect minimization across hundreds of process steps. Traditional approaches relying on empirical process development and reactive maintenance strategies are increasingly inadequate for meeting the stringent requirements of advanced node production, where even minor parameter deviations can result in significant yield losses.
Digital twin technology represents a paradigm shift in addressing these manufacturing challenges by creating virtual replicas of physical plasma etching systems. These sophisticated models integrate real-time sensor data, physics-based simulations, and machine learning algorithms to provide comprehensive insights into process behavior. The convergence of Industry 4.0 principles with semiconductor manufacturing has accelerated the adoption of digital twin solutions, promising enhanced process control, predictive maintenance capabilities, and accelerated process optimization cycles.
The primary objective of implementing digital twins for plasma etching parameter optimization centers on achieving real-time process monitoring and control with unprecedented accuracy. This involves developing comprehensive models that can predict etch rates, profile evolution, and defect formation based on input parameters such as gas flow rates, pressure, power settings, and chamber conditions. The digital twin framework aims to establish closed-loop control systems capable of autonomous parameter adjustment to maintain optimal process windows.
Furthermore, the technology seeks to enable predictive analytics for equipment health monitoring and maintenance scheduling, reducing unplanned downtime and extending equipment lifespan. By correlating process parameters with equipment condition indicators, digital twins can forecast potential failures and recommend preventive actions. The ultimate goal encompasses creating a fully integrated manufacturing ecosystem where plasma etching processes are continuously optimized through data-driven insights, leading to improved yield, reduced cycle times, and enhanced product quality in semiconductor fabrication facilities.
The complexity of plasma etching processes has grown exponentially with advancing technology nodes. Modern semiconductor fabrication facilities face unprecedented challenges in maintaining process stability, yield optimization, and defect minimization across hundreds of process steps. Traditional approaches relying on empirical process development and reactive maintenance strategies are increasingly inadequate for meeting the stringent requirements of advanced node production, where even minor parameter deviations can result in significant yield losses.
Digital twin technology represents a paradigm shift in addressing these manufacturing challenges by creating virtual replicas of physical plasma etching systems. These sophisticated models integrate real-time sensor data, physics-based simulations, and machine learning algorithms to provide comprehensive insights into process behavior. The convergence of Industry 4.0 principles with semiconductor manufacturing has accelerated the adoption of digital twin solutions, promising enhanced process control, predictive maintenance capabilities, and accelerated process optimization cycles.
The primary objective of implementing digital twins for plasma etching parameter optimization centers on achieving real-time process monitoring and control with unprecedented accuracy. This involves developing comprehensive models that can predict etch rates, profile evolution, and defect formation based on input parameters such as gas flow rates, pressure, power settings, and chamber conditions. The digital twin framework aims to establish closed-loop control systems capable of autonomous parameter adjustment to maintain optimal process windows.
Furthermore, the technology seeks to enable predictive analytics for equipment health monitoring and maintenance scheduling, reducing unplanned downtime and extending equipment lifespan. By correlating process parameters with equipment condition indicators, digital twins can forecast potential failures and recommend preventive actions. The ultimate goal encompasses creating a fully integrated manufacturing ecosystem where plasma etching processes are continuously optimized through data-driven insights, leading to improved yield, reduced cycle times, and enhanced product quality in semiconductor fabrication facilities.
Semiconductor FAB Optimization Market Demand Analysis
The semiconductor manufacturing industry faces unprecedented pressure to enhance operational efficiency and reduce production costs while maintaining stringent quality standards. Plasma etching processes, critical for creating precise patterns on semiconductor wafers, represent one of the most complex and parameter-sensitive operations in fabrication facilities. The increasing complexity of advanced node technologies, particularly at 7nm, 5nm, and 3nm processes, has amplified the need for sophisticated optimization solutions that can handle hundreds of interdependent process variables simultaneously.
Market demand for FAB optimization solutions has intensified significantly due to several converging factors. The global semiconductor shortage experienced in recent years highlighted the critical importance of maximizing existing production capacity rather than solely relying on new facility construction. Semiconductor manufacturers are increasingly seeking intelligent solutions that can extract maximum performance from current equipment investments while minimizing costly trial-and-error approaches to process optimization.
Digital twin technology specifically addresses the industry's growing requirement for predictive process control and real-time optimization capabilities. Traditional plasma etching parameter optimization relies heavily on empirical methods and extensive physical experimentation, resulting in substantial material waste, extended development cycles, and suboptimal yield rates. The market demand for digital twin solutions stems from their ability to simulate complex plasma physics interactions virtually, enabling rapid parameter exploration without consuming expensive wafer materials or occupying critical production equipment.
The economic drivers supporting this market demand include rising wafer costs, increasing equipment complexity, and tightening profit margins across the semiconductor value chain. Advanced plasma etching systems can cost tens of millions of dollars, making their efficient utilization paramount for FAB profitability. Digital twins offer the potential to reduce process development time by significant margins while improving overall equipment effectiveness and yield performance.
Furthermore, the industry's transition toward Industry 4.0 principles and smart manufacturing has created substantial appetite for data-driven optimization technologies. Semiconductor manufacturers are actively investing in artificial intelligence and machine learning capabilities to maintain competitive advantages in an increasingly challenging market environment. Digital twin solutions for plasma etching optimization align perfectly with these strategic initiatives, offering measurable returns on investment through improved process stability, reduced scrap rates, and enhanced production throughput.
Market demand for FAB optimization solutions has intensified significantly due to several converging factors. The global semiconductor shortage experienced in recent years highlighted the critical importance of maximizing existing production capacity rather than solely relying on new facility construction. Semiconductor manufacturers are increasingly seeking intelligent solutions that can extract maximum performance from current equipment investments while minimizing costly trial-and-error approaches to process optimization.
Digital twin technology specifically addresses the industry's growing requirement for predictive process control and real-time optimization capabilities. Traditional plasma etching parameter optimization relies heavily on empirical methods and extensive physical experimentation, resulting in substantial material waste, extended development cycles, and suboptimal yield rates. The market demand for digital twin solutions stems from their ability to simulate complex plasma physics interactions virtually, enabling rapid parameter exploration without consuming expensive wafer materials or occupying critical production equipment.
The economic drivers supporting this market demand include rising wafer costs, increasing equipment complexity, and tightening profit margins across the semiconductor value chain. Advanced plasma etching systems can cost tens of millions of dollars, making their efficient utilization paramount for FAB profitability. Digital twins offer the potential to reduce process development time by significant margins while improving overall equipment effectiveness and yield performance.
Furthermore, the industry's transition toward Industry 4.0 principles and smart manufacturing has created substantial appetite for data-driven optimization technologies. Semiconductor manufacturers are actively investing in artificial intelligence and machine learning capabilities to maintain competitive advantages in an increasingly challenging market environment. Digital twin solutions for plasma etching optimization align perfectly with these strategic initiatives, offering measurable returns on investment through improved process stability, reduced scrap rates, and enhanced production throughput.
Current Plasma Etching Parameter Control Challenges
Plasma etching parameter control in semiconductor fabrication facilities faces significant complexity due to the multivariable nature of the process. Traditional control systems struggle to manage the intricate relationships between parameters such as gas flow rates, chamber pressure, RF power, temperature, and electrode gap. These parameters exhibit non-linear interdependencies that create challenges in achieving consistent etch profiles and maintaining process stability across different wafer batches.
Real-time monitoring limitations present another critical challenge in current plasma etching operations. Existing sensor technologies often provide delayed feedback or insufficient spatial resolution to capture the dynamic nature of plasma behavior. The lack of comprehensive real-time data makes it difficult to detect process deviations early, leading to potential yield losses and increased scrap rates. Current monitoring systems typically rely on endpoint detection methods that may not capture subtle variations in etch uniformity or profile evolution.
Process drift and equipment aging significantly impact parameter control effectiveness over time. Plasma chambers experience gradual changes in surface conditions, contamination buildup, and component wear that alter the relationship between set parameters and actual process outcomes. Traditional control algorithms fail to adapt to these evolving conditions, requiring frequent manual recalibration and maintenance interventions that disrupt production schedules.
The complexity of modern semiconductor device architectures, including high aspect ratio features and advanced materials, demands increasingly precise control tolerances. Current parameter control systems often lack the sophistication to handle the tight specifications required for sub-10nm technology nodes. The challenge is compounded by the need to maintain uniformity across large wafer surfaces while accommodating pattern density variations and loading effects.
Integration challenges between different equipment vendors and legacy systems create additional control complications. Many fabrication facilities operate with mixed equipment fleets that use proprietary control interfaces and data formats. This heterogeneity makes it difficult to implement unified parameter optimization strategies and limits the ability to leverage cross-platform learning for process improvement.
Real-time monitoring limitations present another critical challenge in current plasma etching operations. Existing sensor technologies often provide delayed feedback or insufficient spatial resolution to capture the dynamic nature of plasma behavior. The lack of comprehensive real-time data makes it difficult to detect process deviations early, leading to potential yield losses and increased scrap rates. Current monitoring systems typically rely on endpoint detection methods that may not capture subtle variations in etch uniformity or profile evolution.
Process drift and equipment aging significantly impact parameter control effectiveness over time. Plasma chambers experience gradual changes in surface conditions, contamination buildup, and component wear that alter the relationship between set parameters and actual process outcomes. Traditional control algorithms fail to adapt to these evolving conditions, requiring frequent manual recalibration and maintenance interventions that disrupt production schedules.
The complexity of modern semiconductor device architectures, including high aspect ratio features and advanced materials, demands increasingly precise control tolerances. Current parameter control systems often lack the sophistication to handle the tight specifications required for sub-10nm technology nodes. The challenge is compounded by the need to maintain uniformity across large wafer surfaces while accommodating pattern density variations and loading effects.
Integration challenges between different equipment vendors and legacy systems create additional control complications. Many fabrication facilities operate with mixed equipment fleets that use proprietary control interfaces and data formats. This heterogeneity makes it difficult to implement unified parameter optimization strategies and limits the ability to leverage cross-platform learning for process improvement.
Existing Plasma Etching Parameter Optimization Methods
01 Machine learning algorithms for digital twin parameter optimization
Advanced machine learning techniques including neural networks, genetic algorithms, and reinforcement learning are employed to automatically optimize parameters in digital twin models. These algorithms can analyze large datasets from physical systems to identify optimal parameter configurations that improve model accuracy and performance. The optimization process involves iterative learning from real-world data to continuously refine parameter values and enhance the digital twin's predictive capabilities.- Machine learning algorithms for digital twin parameter optimization: Advanced machine learning techniques including neural networks, genetic algorithms, and reinforcement learning are employed to automatically optimize parameters in digital twin models. These algorithms can analyze large datasets from physical systems to identify optimal parameter configurations that improve model accuracy and performance. The optimization process involves iterative learning from real-world data to continuously refine parameter values and enhance the digital twin's predictive capabilities.
- Real-time parameter adjustment and calibration methods: Dynamic parameter adjustment techniques enable digital twins to maintain synchronization with their physical counterparts through continuous calibration processes. These methods involve real-time data acquisition, parameter sensitivity analysis, and automated adjustment mechanisms that ensure the digital model accurately reflects the current state of the physical system. The calibration process includes feedback loops and adaptive algorithms that respond to changing operational conditions.
- Multi-objective optimization frameworks for digital twin systems: Comprehensive optimization frameworks that simultaneously consider multiple objectives such as accuracy, computational efficiency, and resource utilization in digital twin parameter tuning. These frameworks employ various optimization strategies including Pareto optimization, weighted objective functions, and constraint handling techniques to balance competing requirements. The approach enables optimal parameter selection while considering trade-offs between different performance metrics.
- Sensor data integration and parameter estimation techniques: Advanced methodologies for integrating heterogeneous sensor data to estimate and optimize digital twin parameters through statistical and probabilistic approaches. These techniques involve data fusion algorithms, uncertainty quantification methods, and parameter estimation frameworks that process multiple data streams to derive optimal parameter values. The integration process includes data preprocessing, feature extraction, and parameter inference mechanisms.
- Cloud-based and distributed optimization architectures: Scalable cloud computing and distributed processing architectures designed specifically for digital twin parameter optimization tasks. These systems leverage distributed computing resources, parallel processing capabilities, and cloud-native optimization services to handle complex parameter optimization problems efficiently. The architecture includes load balancing, resource allocation, and distributed algorithm execution frameworks that enable large-scale optimization operations.
02 Real-time parameter adjustment and calibration methods
Dynamic parameter adjustment techniques enable digital twins to maintain synchronization with their physical counterparts through continuous calibration processes. These methods involve real-time data acquisition, parameter sensitivity analysis, and automated adjustment mechanisms that ensure the digital model accurately reflects the current state of the physical system. The calibration process includes feedback loops and adaptive algorithms that respond to changing operational conditions.Expand Specific Solutions03 Multi-objective optimization frameworks for complex systems
Comprehensive optimization frameworks that handle multiple conflicting objectives simultaneously in digital twin parameter tuning. These approaches utilize advanced mathematical optimization techniques to balance various performance criteria such as accuracy, computational efficiency, and resource utilization. The frameworks incorporate constraint handling, Pareto optimization, and decision-making algorithms to find optimal trade-offs between competing objectives in complex industrial systems.Expand Specific Solutions04 Uncertainty quantification and robust parameter estimation
Statistical methods and probabilistic approaches for handling uncertainty in digital twin parameter optimization processes. These techniques account for measurement noise, model uncertainties, and variability in system behavior to provide robust parameter estimates. The methods include Bayesian inference, Monte Carlo simulations, and sensitivity analysis to quantify confidence levels and ensure reliable parameter optimization under uncertain conditions.Expand Specific Solutions05 Cloud-based and distributed optimization architectures
Scalable computing architectures that leverage cloud resources and distributed processing for large-scale digital twin parameter optimization. These systems enable parallel processing of optimization tasks, efficient resource allocation, and collaborative optimization across multiple digital twin instances. The architectures support high-performance computing requirements and provide flexible deployment options for enterprise-level digital twin applications.Expand Specific Solutions
Key Players in Digital Twin Semiconductor Solutions
The digital twins for plasma etching parameter optimization market represents an emerging intersection of advanced manufacturing and digital simulation technologies within the semiconductor industry. Currently in its early development stage, this market is experiencing rapid growth driven by increasing demand for precision in semiconductor fabrication processes. The market size remains relatively modest but shows significant expansion potential as semiconductor manufacturers seek enhanced process control and yield optimization. Technology maturity varies considerably across market participants, with established equipment manufacturers like Lam Research Corp., Applied Materials Inc., and Tokyo Electron Ltd. leading in traditional plasma etching systems while beginning to integrate digital twin capabilities. Foundry leaders such as Taiwan Semiconductor Manufacturing Co. and Samsung Electronics are driving adoption through implementation requirements. Meanwhile, specialized software companies like Silvaco Inc. and research institutions including various universities are advancing the underlying simulation and modeling technologies. The competitive landscape reflects a transitional phase where traditional hardware expertise is converging with advanced digital modeling capabilities.
Lam Research Corp.
Technical Solution: Lam Research has developed advanced digital twin solutions for plasma etching processes that integrate real-time sensor data with physics-based models to optimize etch uniformity and process stability. Their digital twin platform combines machine learning algorithms with plasma physics simulations to predict etch rates, selectivity, and profile evolution across wafer surfaces. The system continuously learns from production data to refine process parameters, reducing within-wafer non-uniformity by up to 15% and improving chamber matching performance. Their approach incorporates multi-physics modeling of plasma chemistry, ion transport, and surface reactions to provide comprehensive process optimization capabilities for advanced semiconductor manufacturing nodes.
Strengths: Industry-leading plasma etching expertise with comprehensive sensor integration and proven production deployment. Weaknesses: High implementation costs and complexity requiring specialized technical expertise for operation and maintenance.
Applied Materials, Inc.
Technical Solution: Applied Materials has implemented digital twin technology through their Productivity Plus platform, which creates virtual replicas of plasma etching chambers to optimize process parameters in real-time. Their solution combines advanced process control algorithms with predictive analytics to maintain consistent etch performance across multiple chambers and fabs. The digital twin system utilizes machine learning models trained on extensive historical process data to predict optimal gas flow rates, pressure settings, and RF power levels for specific device structures. The platform enables predictive maintenance by monitoring chamber condition indicators and can reduce process variation by up to 20% while extending equipment uptime through optimized cleaning cycles and component replacement scheduling.
Strengths: Comprehensive equipment portfolio with integrated digital solutions and strong data analytics capabilities across multiple process steps. Weaknesses: Platform complexity may require significant training and integration challenges with existing fab infrastructure.
Core Digital Twin Modeling Technologies for Plasma Processes
Creating a Digital Twin of Semiconductor Manufacturing Equipment
PatentPendingJP2024504598A
Innovation
- A digital twin of the process chamber is generated using a combination of AI/ML models, HFS models, and closed-form solutions, each representing different classes of physical phenomena like thermal, plasma, fluid dynamics, and chemical reactions, to accurately simulate the chamber's operations.
System and Method for Atomic Layer Etching with Autonomous Process Recipe Generation
PatentPendingUS20250391647A1
Innovation
- Integration of a system digital twin that autonomously generates and adjusts process recipe parameters and subsystem control parameters, utilizing neural networks and simulation models to predict and optimize etching outcomes.
Semiconductor Industry Standards and Compliance Requirements
The semiconductor industry operates under stringent regulatory frameworks that directly impact the implementation of digital twin technologies for plasma etching parameter optimization. The International Technology Roadmap for Semiconductors (ITRS) and its successor, the International Roadmap for Devices and Systems (IRDS), establish fundamental guidelines for manufacturing process control and metrology requirements. These roadmaps mandate specific tolerances for critical dimensions, uniformity specifications, and process repeatability metrics that digital twin systems must accommodate.
SEMI standards play a crucial role in defining equipment communication protocols and data exchange formats essential for digital twin integration. SEMI E125 establishes requirements for advanced process control systems, while SEMI E164 specifies data collection and analysis protocols that digital twin platforms must support. The Generic Equipment Model (GEM) standard SEMI E30 defines the communication interface between fab equipment and host systems, ensuring seamless data flow for real-time parameter optimization.
Quality management systems compliance, particularly ISO 9001 and automotive-specific IATF 16949, impose rigorous documentation and traceability requirements on plasma etching processes. Digital twin implementations must maintain comprehensive audit trails, process genealogy records, and statistical process control data to meet these certification requirements. The systems must demonstrate capability studies, measurement system analysis, and continuous improvement methodologies.
Environmental and safety regulations significantly influence digital twin system design and operation. The Restriction of Hazardous Substances (RoHS) directive and REACH regulations affect material selection and process chemistry optimization algorithms. Occupational Safety and Health Administration (OSHA) requirements mandate real-time monitoring of plasma chamber conditions and automated safety interlocks that digital twin systems must integrate.
Export control regulations, including the Export Administration Regulations (EAR) and International Traffic in Arms Regulations (ITAR), impose restrictions on technology transfer and data sharing capabilities within digital twin platforms. These regulations particularly affect cloud-based implementations and international collaboration features, requiring robust data sovereignty and access control mechanisms.
Cybersecurity compliance frameworks such as NIST Cybersecurity Framework and IEC 62443 establish mandatory security controls for industrial automation systems. Digital twin platforms must implement multi-layered security architectures, including network segmentation, encrypted communications, and role-based access controls to protect sensitive process intellectual property and prevent unauthorized parameter modifications.
SEMI standards play a crucial role in defining equipment communication protocols and data exchange formats essential for digital twin integration. SEMI E125 establishes requirements for advanced process control systems, while SEMI E164 specifies data collection and analysis protocols that digital twin platforms must support. The Generic Equipment Model (GEM) standard SEMI E30 defines the communication interface between fab equipment and host systems, ensuring seamless data flow for real-time parameter optimization.
Quality management systems compliance, particularly ISO 9001 and automotive-specific IATF 16949, impose rigorous documentation and traceability requirements on plasma etching processes. Digital twin implementations must maintain comprehensive audit trails, process genealogy records, and statistical process control data to meet these certification requirements. The systems must demonstrate capability studies, measurement system analysis, and continuous improvement methodologies.
Environmental and safety regulations significantly influence digital twin system design and operation. The Restriction of Hazardous Substances (RoHS) directive and REACH regulations affect material selection and process chemistry optimization algorithms. Occupational Safety and Health Administration (OSHA) requirements mandate real-time monitoring of plasma chamber conditions and automated safety interlocks that digital twin systems must integrate.
Export control regulations, including the Export Administration Regulations (EAR) and International Traffic in Arms Regulations (ITAR), impose restrictions on technology transfer and data sharing capabilities within digital twin platforms. These regulations particularly affect cloud-based implementations and international collaboration features, requiring robust data sovereignty and access control mechanisms.
Cybersecurity compliance frameworks such as NIST Cybersecurity Framework and IEC 62443 establish mandatory security controls for industrial automation systems. Digital twin platforms must implement multi-layered security architectures, including network segmentation, encrypted communications, and role-based access controls to protect sensitive process intellectual property and prevent unauthorized parameter modifications.
Real-time Process Monitoring and Feedback Systems
Real-time process monitoring and feedback systems represent the operational backbone of digital twin implementations in plasma etching environments. These systems continuously capture critical process parameters including plasma density, ion energy distribution, gas flow rates, chamber pressure, and substrate temperature through an array of sophisticated sensors and diagnostic tools. Advanced optical emission spectroscopy, mass spectrometry, and electrical probe measurements provide millisecond-level data acquisition capabilities essential for maintaining process stability.
The integration of machine learning algorithms enables predictive analytics within these monitoring frameworks, allowing for proactive identification of process drift before it impacts wafer quality. Neural networks trained on historical process data can detect subtle pattern variations that indicate impending equipment malfunctions or recipe deviations. This predictive capability significantly reduces the occurrence of out-of-specification wafers and minimizes costly production interruptions.
Feedback control mechanisms utilize real-time data streams to automatically adjust process parameters within predetermined tolerance windows. Closed-loop control systems can modify RF power, gas chemistry ratios, and chamber conditions in response to detected variations, maintaining optimal etching performance without human intervention. The response time of these systems typically ranges from 10 to 100 milliseconds, ensuring rapid correction of process deviations.
Data fusion techniques combine information from multiple sensor sources to create comprehensive process signatures that enhance monitoring accuracy. Statistical process control methods integrated with these systems provide early warning capabilities for process excursions, enabling fab operators to implement corrective actions before yield impacts occur. The implementation of edge computing architectures reduces latency in feedback loops, ensuring that critical process adjustments occur within the narrow time windows required for effective plasma etching control.
Modern monitoring systems also incorporate advanced visualization tools that present complex multi-dimensional process data in intuitive formats, facilitating rapid decision-making by process engineers and equipment technicians during production operations.
The integration of machine learning algorithms enables predictive analytics within these monitoring frameworks, allowing for proactive identification of process drift before it impacts wafer quality. Neural networks trained on historical process data can detect subtle pattern variations that indicate impending equipment malfunctions or recipe deviations. This predictive capability significantly reduces the occurrence of out-of-specification wafers and minimizes costly production interruptions.
Feedback control mechanisms utilize real-time data streams to automatically adjust process parameters within predetermined tolerance windows. Closed-loop control systems can modify RF power, gas chemistry ratios, and chamber conditions in response to detected variations, maintaining optimal etching performance without human intervention. The response time of these systems typically ranges from 10 to 100 milliseconds, ensuring rapid correction of process deviations.
Data fusion techniques combine information from multiple sensor sources to create comprehensive process signatures that enhance monitoring accuracy. Statistical process control methods integrated with these systems provide early warning capabilities for process excursions, enabling fab operators to implement corrective actions before yield impacts occur. The implementation of edge computing architectures reduces latency in feedback loops, ensuring that critical process adjustments occur within the narrow time windows required for effective plasma etching control.
Modern monitoring systems also incorporate advanced visualization tools that present complex multi-dimensional process data in intuitive formats, facilitating rapid decision-making by process engineers and equipment technicians during production operations.
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