Semiconductor manufacturing is a complex, high-tech process that generates a large volume of data. Utilizing this data effectively is critical for improving production yield, maintaining product quality, and driving efficiency across operations. Enter big-data analytics. While the term “big data” often refers to vast data sets that are too large for traditional data-processing tools to handle, its importance in the semiconductor manufacturing industry can't be understated. Big-data and yield analytics not only provides ways to process, analyze, and draw insights from these large volumes of data but also facilitates more efficient decision-making, informed by detailed, real-time data insights.
Overview of Big Data Analytics in Semiconductor Yield Management.pptxyieldWerx
As semiconductor manufacturing yield advances, the industry is increasingly turning to big data analytics to manage yield and improve productivity. By understanding the relationship between process variation and device performance, manufacturers can identify areas for improvement and make informed decisions about process changes.
Data Integration in Manufacturing: Enhancing Efficiency and Quality.pptMileyJames
In the modern manufacturing landscape, data integration has emerged as a transformative force. The ability to seamlessly collect, aggregate, and analyze data from various sources within a manufacturing facility is revolutionizing the industry. Data integration in manufacturing not only drives efficiency and cost reduction but also significantly enhances product quality and innovation. This essay delves into the importance, benefits, challenges, and future prospects of data integration in the manufacturing sector.
Unleashing the Power of Manufacturing Analytics: Transforming Industry Throug...MileyJames
Manufacturing has always been at the forefront of technological innovation, from the advent of the Industrial Revolution to the modern age of automation and digitalization. Today, the manufacturing industry is undergoing a profound transformation driven by the integration of advanced analytics and data-driven decision-making processes. Manufacturing analytics, a powerful subset of industrial analytics, is paving the way for smarter, more efficient, and agile manufacturing operations. In this article, we explore the concept of manufacturing analytics, its significance, and the ways it is reshaping the future of manufacturing.
Conquering Chip Complexity with Data Analytics A New Approach to Semiconducto...yieldWerx Semiconductor
The silicon manufacturing process's rising complexity is leading to an explosion of data, causing significant challenges for engineers. These challenges arise from insufficient access to comprehensive lifecycle data and the difficulties in mining valuable insights from vast amounts of raw data. This is particularly significant in sectors like automotive, where the semiconductor industry is progressively transitioning towards a Zero Defect tools semiconductor approach. Such an approach necessitates robust data analytics solutions to tackle yield and quality issues efficiently and effectively (Pierret, 1996).
Innovating Quality Control in the Semiconductor Manufacturing Industry.pptxyieldWerx Semiconductor
The semiconductor manufacturing industry, a high-volume manufacturing environment characterized by its intricacy, stands as a testament to precision and performance. To ensure optimal outcomes, it is vital to maintain consistent quality control, with a special emphasis on the rectification of tool deterioration. Implementing innovative strategies related to process control monitoring can mitigate this problem and set a path towards a 'zero equipment failure' environment.
Overview of Big Data Analytics in Semiconductor Yield Management.pptxyieldWerx
As semiconductor manufacturing yield advances, the industry is increasingly turning to big data analytics to manage yield and improve productivity. By understanding the relationship between process variation and device performance, manufacturers can identify areas for improvement and make informed decisions about process changes.
Data Integration in Manufacturing: Enhancing Efficiency and Quality.pptMileyJames
In the modern manufacturing landscape, data integration has emerged as a transformative force. The ability to seamlessly collect, aggregate, and analyze data from various sources within a manufacturing facility is revolutionizing the industry. Data integration in manufacturing not only drives efficiency and cost reduction but also significantly enhances product quality and innovation. This essay delves into the importance, benefits, challenges, and future prospects of data integration in the manufacturing sector.
Unleashing the Power of Manufacturing Analytics: Transforming Industry Throug...MileyJames
Manufacturing has always been at the forefront of technological innovation, from the advent of the Industrial Revolution to the modern age of automation and digitalization. Today, the manufacturing industry is undergoing a profound transformation driven by the integration of advanced analytics and data-driven decision-making processes. Manufacturing analytics, a powerful subset of industrial analytics, is paving the way for smarter, more efficient, and agile manufacturing operations. In this article, we explore the concept of manufacturing analytics, its significance, and the ways it is reshaping the future of manufacturing.
Conquering Chip Complexity with Data Analytics A New Approach to Semiconducto...yieldWerx Semiconductor
The silicon manufacturing process's rising complexity is leading to an explosion of data, causing significant challenges for engineers. These challenges arise from insufficient access to comprehensive lifecycle data and the difficulties in mining valuable insights from vast amounts of raw data. This is particularly significant in sectors like automotive, where the semiconductor industry is progressively transitioning towards a Zero Defect tools semiconductor approach. Such an approach necessitates robust data analytics solutions to tackle yield and quality issues efficiently and effectively (Pierret, 1996).
Innovating Quality Control in the Semiconductor Manufacturing Industry.pptxyieldWerx Semiconductor
The semiconductor manufacturing industry, a high-volume manufacturing environment characterized by its intricacy, stands as a testament to precision and performance. To ensure optimal outcomes, it is vital to maintain consistent quality control, with a special emphasis on the rectification of tool deterioration. Implementing innovative strategies related to process control monitoring can mitigate this problem and set a path towards a 'zero equipment failure' environment.
IoT in manufacturing is changing the business which include how they conduct all their operations and our IoT solution in manufacturing takes the data collection a step further. We guide you to gather all the data operations, production, utilization, consumption, etc from various things and utilize it and improve the business processes. Other significant information regarding various assets and resources has not been reported or not recorded. This information relates to the manufacturing assets and resources, but we Rootfacts make sure all these important data never get wasted.
Data Science In Manufacturing industries allows the manufacturers to fasten the process of manufacturing and managing data of suppliers, customers, and business partners. Many manufacturing industries are undergoing a huge transformation due to digital technology and have a good number of sales have to manage data in huge quantities. With the help of data science, they can easily manage data from both external and internal sources, from processors and detectors to enhance their production process, decrease energy costs, and increase their sales.
Semiconductor manufacturing and semiconductor yield management is becoming more complex due to relentless advancements in technology. The ability to control critical dimensions is becoming increasingly important yet challenging as manufacturing processes continue to evolve. New production processes and variable machine configurations contribute to the complexity, generating high-dimensional, multi-collinear data that are difficult to analyze.
This intricate web of process data can be a hindrance in identifying the root causes of low yields or "excursions." However, data-driven methodologies present a powerful solution for these challenges. The implementation of big data analytics and machine learning techniques can help parse the overwhelming amount of data and extract insightful conclusions from it.
Unlock the potential of Big Data with Edvicon. Learn the benefits of harnessing vast information, from our expert instructors. Gain valuable insights and make data-driven decisions for future success.
visit us-https://edvicon.in/
Streamlining Operations: The Role of Data Integration in the Manufacturing Wo...MileyJames
In today's rapidly evolving manufacturing landscape, data has become a valuable asset. The ability to collect, analyze, and utilize data effectively can significantly impact a manufacturer's ability to make informed decisions, improve processes, and remain competitive. Data integration plays a crucial role in this transformation, serving as the linchpin that connects disparate systems and empowers manufacturers to harness the full potential of their data. In this article, we will explore the importance of data integration in the manufacturing world and how it is revolutionizing the industry.
Key Facts About Big Data Analytics You Need to Know.pdfAssignment World
Big data analytics involves examining large and varied data sets to uncover hidden patterns, correlations, and insights that drive strategic business decisions. Key facts include the massive volume of data generated daily, the variety of data types from numerous sources, the speed of real-time data processing, and the critical importance of data accuracy.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...ijdpsjournal
The paper aims at proposing a solution for designing and developing a seamless automation and
integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to
seamlessly handle and scale very large amount of unstructured and structured data from diversified and
heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing
component; 3) the ability to automatically select the most appropriate libraries and tools to compute and
accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high
learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different
application problem domains, with high accuracy, robustness, and scalability. This paper highlights the
research methodologies and research activities that we propose to be conducted by the Big Data
researchers and practitioners in order to develop and support seamless automation and integration of
machine learning capabilities for Big Data analytics.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...ijdpsjournal
The paper aims at proposing a solution for designing and developing a seamless automation and integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to seamlessly handle and scale very large amount of unstructured and structured data from diversified and heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing component; 3) the ability to automatically select the most appropriate libraries and tools to compute and accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different application problem domains, with high accuracy, robustness, and scalability. This paper highlights the research methodologies and research activities that we propose to be conducted by the Big Data researchers and practitioners in order to develop and support seamless automation and integration of machine learning capabilities for Big Data analytics.
Manufacturing and the data conundrum: Too much? Too little? Or just right?, commissioned by Wipro, examines how manufacturers are using integrated data collection and analysis to improve production throughput, reduce costs and improve quality. The research is based on a survey of 50 C-suite executives from manufacturers in North America and Western Europe.
The survey shows that just 42% of respondents have what they consider to be a well-defined data-management strategy. However, 62% are not sure they have been able to keep up with the large volumes of data they collect, as it comes from too many sources and in a variety of formats and speeds.
The report also finds that while over 90% of manufacturers collect data from monitoring production processes, less than half have in place predictive data analytics, and less than 40% use data analysis to find solutions to production problems.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Advanced Methods for Outlier Detection and Analysis in Semiconductor Manufact...yieldWerx Semiconductor
The semiconductor manufacturing industry faces numerous challenges due to its complex equipment and dynamic processes. To overcome these challenges and enhance operational efficiency, there is a growing emphasis on integrating domain expertise and utilizing advanced analytical solutions. This article explores the concept of outliers in semiconductor manufacturing, delves into outlier detection methods, highlights the significance of outlier analysis in semiconductor yield monitoring, and discusses the role of semiconductor data in driving effective analytics.
A Holistic Approach to Yield Improvement in the Semiconductor Manufacturing I...yieldWerx Semiconductor
Semiconductor manufacturing is one of the most complex and competitive industries, heavily driven by innovation and cost-efficiency. It is continuously grappling with increasing cost pressures while concurrently working to meet the demands of rapidly advancing technology. Yield optimization, a multifaceted process aimed at improving the number of usable chips produced from raw materials, is an integral part of reducing manufacturing costs. This process involves taking into account several elements, such as equipment performance, operator capability, and the complexity of the design.
Data Insight-Driven Project Delivery ACADIA 2017gapariciojr
Today, 98% of megaprojects face cost overruns or delays. The average cost increase is 80% and the average slippage is 20 months behind schedule (McKinsey 2015). It is becoming increasingly challenging to efficiently support the scale, complexity and ambition of these projects. Simultaneously, project data is being captured at growing rates. We continue to capture more data on a project than ever before. Total data captured back in 2009 in the construction industry reached over 51 petabytes, or 51 million gigabytes (Mckinsey 2016). It is becoming increasingly necessary to develop new ways to leverage our project data to better manage the complexity on our projects and allow the many stakeholders to make better more informed decisions. This paper focuses on utilizing advances in data mining, data analytics and data visualization as means to extract project information from massive datasets in a timely fashion to assist in making key informed decisions for project delivery. As part of this paper, we present an innovative new use of these technologies as applied to a large-scale infrastructural megaproject, to deliver a set of over 4,000 construction documents in a six-month period that has the potential to dramatically transform our industry and the way we deliver projects in the future. This presentation describes a framework used to measure production performance as part of any project’s set of project controls for accelerated project delivery.
When it comes to product development, companies have long relied on traditional tools and approaches. By incorporating predictive analytics into the process, organizations can sharpen their forecasts; better predict product performance, failures, and downtime; and generate more value for the business and its customers. Yet doing so requires companies to thoroughly assess their strategic goals, their appetite for investment, and their willingness to experiment.
Enhancing Quality Control with Statistical Process Control (SPC) in the Semic...yieldWerx Semiconductor
Statistical Process Control Semiconductor (SPC) is a critical methodology in the realm of quality control, especially in the semiconductor manufacturing industry, that allows for a systematic approach to process improvement through the use of statistical analysis. The purpose of SPC is to get a comprehensive understanding of the variability in a process to enhance and ensure product quality, thereby positively impacting the overall performance of a manufacturing company.
Harnessing the Power of Yield Management and Statistical Process Control in S...yieldWerx Semiconductor
Semiconductor manufacturing sits at the nexus of technology, powering an array of devices that shape our modern world, from sophisticated Internet of Things (IoT) appliances to powerful computing systems. Navigating the high-demand landscape and ensuring an efficient production pipeline poses unique challenges due to the complex and intricate nature of semiconductor device fabrication. To meet these challenges, industry leaders employ advanced strategies such as yield management and statistical process control (SPC semiconductor). These key tools help maintain high yield rates, minimize defect densities, and optimize process parameters. In this in-depth exploration, we will shed light on the critical role of these statistical and analytical methodologies, examining their utilization for data-driven decision-making, process stability assessment, and system optimization in the semiconductor manufacturing arena.
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IoT in manufacturing is changing the business which include how they conduct all their operations and our IoT solution in manufacturing takes the data collection a step further. We guide you to gather all the data operations, production, utilization, consumption, etc from various things and utilize it and improve the business processes. Other significant information regarding various assets and resources has not been reported or not recorded. This information relates to the manufacturing assets and resources, but we Rootfacts make sure all these important data never get wasted.
Data Science In Manufacturing industries allows the manufacturers to fasten the process of manufacturing and managing data of suppliers, customers, and business partners. Many manufacturing industries are undergoing a huge transformation due to digital technology and have a good number of sales have to manage data in huge quantities. With the help of data science, they can easily manage data from both external and internal sources, from processors and detectors to enhance their production process, decrease energy costs, and increase their sales.
Semiconductor manufacturing and semiconductor yield management is becoming more complex due to relentless advancements in technology. The ability to control critical dimensions is becoming increasingly important yet challenging as manufacturing processes continue to evolve. New production processes and variable machine configurations contribute to the complexity, generating high-dimensional, multi-collinear data that are difficult to analyze.
This intricate web of process data can be a hindrance in identifying the root causes of low yields or "excursions." However, data-driven methodologies present a powerful solution for these challenges. The implementation of big data analytics and machine learning techniques can help parse the overwhelming amount of data and extract insightful conclusions from it.
Unlock the potential of Big Data with Edvicon. Learn the benefits of harnessing vast information, from our expert instructors. Gain valuable insights and make data-driven decisions for future success.
visit us-https://edvicon.in/
Streamlining Operations: The Role of Data Integration in the Manufacturing Wo...MileyJames
In today's rapidly evolving manufacturing landscape, data has become a valuable asset. The ability to collect, analyze, and utilize data effectively can significantly impact a manufacturer's ability to make informed decisions, improve processes, and remain competitive. Data integration plays a crucial role in this transformation, serving as the linchpin that connects disparate systems and empowers manufacturers to harness the full potential of their data. In this article, we will explore the importance of data integration in the manufacturing world and how it is revolutionizing the industry.
Key Facts About Big Data Analytics You Need to Know.pdfAssignment World
Big data analytics involves examining large and varied data sets to uncover hidden patterns, correlations, and insights that drive strategic business decisions. Key facts include the massive volume of data generated daily, the variety of data types from numerous sources, the speed of real-time data processing, and the critical importance of data accuracy.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...ijdpsjournal
The paper aims at proposing a solution for designing and developing a seamless automation and
integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to
seamlessly handle and scale very large amount of unstructured and structured data from diversified and
heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing
component; 3) the ability to automatically select the most appropriate libraries and tools to compute and
accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high
learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different
application problem domains, with high accuracy, robustness, and scalability. This paper highlights the
research methodologies and research activities that we propose to be conducted by the Big Data
researchers and practitioners in order to develop and support seamless automation and integration of
machine learning capabilities for Big Data analytics.
SEAMLESS AUTOMATION AND INTEGRATION OF MACHINE LEARNING CAPABILITIES FOR BIG ...ijdpsjournal
The paper aims at proposing a solution for designing and developing a seamless automation and integration of machine learning capabilities for Big Data with the following requirements: 1) the ability to seamlessly handle and scale very large amount of unstructured and structured data from diversified and heterogeneous sources; 2) the ability to systematically determine the steps and procedures needed for
analyzing Big Data datasets based on data characteristics, domain expert inputs, and data pre-processing component; 3) the ability to automatically select the most appropriate libraries and tools to compute and accelerate the machine learning computations; and 4) the ability to perform Big Data analytics with high learning performance, but with minimal human intervention and supervision. The whole focus is to provide
a seamless automated and integrated solution which can be effectively used to analyze Big Data with highfrequency
and high-dimensional features from different types of data characteristics and different application problem domains, with high accuracy, robustness, and scalability. This paper highlights the research methodologies and research activities that we propose to be conducted by the Big Data researchers and practitioners in order to develop and support seamless automation and integration of machine learning capabilities for Big Data analytics.
Manufacturing and the data conundrum: Too much? Too little? Or just right?, commissioned by Wipro, examines how manufacturers are using integrated data collection and analysis to improve production throughput, reduce costs and improve quality. The research is based on a survey of 50 C-suite executives from manufacturers in North America and Western Europe.
The survey shows that just 42% of respondents have what they consider to be a well-defined data-management strategy. However, 62% are not sure they have been able to keep up with the large volumes of data they collect, as it comes from too many sources and in a variety of formats and speeds.
The report also finds that while over 90% of manufacturers collect data from monitoring production processes, less than half have in place predictive data analytics, and less than 40% use data analysis to find solutions to production problems.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Advanced Methods for Outlier Detection and Analysis in Semiconductor Manufact...yieldWerx Semiconductor
The semiconductor manufacturing industry faces numerous challenges due to its complex equipment and dynamic processes. To overcome these challenges and enhance operational efficiency, there is a growing emphasis on integrating domain expertise and utilizing advanced analytical solutions. This article explores the concept of outliers in semiconductor manufacturing, delves into outlier detection methods, highlights the significance of outlier analysis in semiconductor yield monitoring, and discusses the role of semiconductor data in driving effective analytics.
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Semiconductor manufacturing is one of the most complex and competitive industries, heavily driven by innovation and cost-efficiency. It is continuously grappling with increasing cost pressures while concurrently working to meet the demands of rapidly advancing technology. Yield optimization, a multifaceted process aimed at improving the number of usable chips produced from raw materials, is an integral part of reducing manufacturing costs. This process involves taking into account several elements, such as equipment performance, operator capability, and the complexity of the design.
Data Insight-Driven Project Delivery ACADIA 2017gapariciojr
Today, 98% of megaprojects face cost overruns or delays. The average cost increase is 80% and the average slippage is 20 months behind schedule (McKinsey 2015). It is becoming increasingly challenging to efficiently support the scale, complexity and ambition of these projects. Simultaneously, project data is being captured at growing rates. We continue to capture more data on a project than ever before. Total data captured back in 2009 in the construction industry reached over 51 petabytes, or 51 million gigabytes (Mckinsey 2016). It is becoming increasingly necessary to develop new ways to leverage our project data to better manage the complexity on our projects and allow the many stakeholders to make better more informed decisions. This paper focuses on utilizing advances in data mining, data analytics and data visualization as means to extract project information from massive datasets in a timely fashion to assist in making key informed decisions for project delivery. As part of this paper, we present an innovative new use of these technologies as applied to a large-scale infrastructural megaproject, to deliver a set of over 4,000 construction documents in a six-month period that has the potential to dramatically transform our industry and the way we deliver projects in the future. This presentation describes a framework used to measure production performance as part of any project’s set of project controls for accelerated project delivery.
When it comes to product development, companies have long relied on traditional tools and approaches. By incorporating predictive analytics into the process, organizations can sharpen their forecasts; better predict product performance, failures, and downtime; and generate more value for the business and its customers. Yet doing so requires companies to thoroughly assess their strategic goals, their appetite for investment, and their willingness to experiment.
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The semiconductor industry is a cornerstone of modern technology, with applications ranging from consumer electronics to medical equipment. With the level of precision required in this sector, the demand for maintaining high-quality standards and minimizing variability is immense. One of the key statistical tools used in this pursuit of perfection is Gauge Repeatability and Reproducibility (Gauge R&R), which plays an indispensable role for improved yield in semiconductor manufacturing.
The criticality of Gauge R&R emerges from its ability to assess the deviations and variability caused by measurement systems, tools, or operators. It ensures that measurements taken by testing devices remain within specified tolerances, hence maintaining production consistency and mitigating potential losses due to faulty products.
Analytics Solutions for the Semiconductor Manufacturing Industry.pptxyieldWerx Semiconductor
The semiconductor industry faces several challenges that impact the effectiveness of yield analytics solutions. These challenges include equipment and process complexity, process dynamics, and data quality. To overcome these challenges, the industry recognizes the need for domain or subject matter expertise (SME) in tool process and analytics.
Analytics and yms solutions are crucial for addressing the challenges in the semiconductor data manufacturing industry. These yield management solutions leverage advanced techniques and subject matter expertise to overcome complexity, manage process dynamics, and improve data quality. By incorporating expertise, analytics solutions effectively analyze and control the semiconductor manufacturing process. Next-generation Fault Detection and Classification (NG-FDC) techniques offer improved accuracy and efficiency by incorporating automated analysis and SME knowledge. Overall, integrating subject matter expertise is essential for achieving robust manufacturing processes and enhanced performance in the semiconductor industry.
Understanding the Dynamics of Semiconductor Manufacturing Yield Analysis and ...yieldWerx Semiconductor
The science and art of semiconductor manufacturing, responsible for powering the digital revolution, constitute one of the most intricate, detailed, and complicated processes of the modern industrial world. The essence of this complex multistage operation lies in creating integrated circuits, miniature electronic circuits that have found their way into almost every electronic device. These electronic marvels govern our digital world, from computers and smartphones to cars and kitchen appliances, and beyond.
The manufacturing of these semiconductors is a fascinating process that involves several layers of science - physics, chemistry, materials science - and combines them with precision engineering to create devices that are continuously shrinking in size while increasing in capabilities. This process is a testament to human ingenuity, involving billions of dollars of advanced equipment, and is growing increasingly complex due to reductions in feature size and the rising number of devices. However, amid this complexity, certain aspects stand out due to their impact on the manufacturing process: Semiconductor Yield Analysis, the Fabless Semiconductor business model, and Yield Management Systems.
Enhancing Yield in IC Design and Elevating YMS with AI and Machine Learning.pptxyieldWerx Semiconductor
In the rapidly evolving landscape of semiconductor manufacturing, two key areas stand at the forefront of driving efficiency and productivity - Yield in Integrated Circuit (IC) design and the use of artificial intelligence (AI) and machine learning in Yield Management Systems (YMS). Enhancing the yield of ICs during the design stage and incorporating advanced AI techniques in YMS can significantly transform the semiconductor manufacturing process, leading to improved operational efficiency, reduced costs, and high-quality products. This article delves into these critical areas, exploring how optimizing IC design can maximize yield and how AI and machine learning can augment YMS to unlock new levels of productivity and efficiency in semiconductor manufacturing.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. Semiconductor manufacturing is a complex, high-tech process that generates a large volume of data. Utilizing this data effectively
is critical for improving production yield, maintaining product quality, and driving efficiency across operations. Enter big-data
analytics. While the term “big data” often refers to vast data sets that are too large for traditional data-processing tools to
handle, its importance in the semiconductor manufacturing industry can't be understated. Big-data and yield analytics not only
provides ways to process, analyze, and draw insights from these large volumes of data but also facilitates more efficient decision-
making, informed by detailed, real-time data insights.
The key to understanding the role of big data semiconductor manufacturing begins with appreciating the complex nature of the
industry. Semiconductor manufacturing is a sophisticated, multi-stage process. During each stage, a vast array of parameters
must be closely monitored and controlled to ensure the final product's quality and reliability. This process inevitably produces
enormous amounts of data, which can be classified into different dimensions based on the source, type, and purpose of the data.
Data dimensions include manufacturing yield data, test data, product genealogy data, and more.
Big Data Dimensions in Semiconductor Manufacturing
Semiconductor data can be derived from various sources, such as the production line where product genealogy data comes from,
which includes data on material batches, production equipment, production shifts, and production times. Test data, on the other
hand, comes from a variety of tests performed throughout the manufacturing process, such as electrical tests, optical tests, and
physical tests. Each test produces a wealth of data that can be further analyzed to understand product performance and quality.
Data Dimensions in Semiconductor Manufacturing
Understanding and navigating through these data dimensions require robust data analysis tools that can process large volumes of
data, perform complex computations, and deliver insights in real time. Tools like HP Vertica and cloud-based solutions are
frequently deployed in the industry due to their scalability and ability to handle large, complex data sets.
3. Yield Analytics and Root Cause Analysis in Semiconductor Manufacturing
Yield analytics is another critical aspect of semiconductor manufacturing. Yield refers to the percentage of chips in a wafer that
are free from defects. Improving yield is a primary objective in semiconductor manufacturing since higher yield translates to
lower costs and higher profitability. To improve semiconductor yield, manufacturers need to identify the root causes of defects,
and this is where big data analytics comes in.
By using semiconductor big data analytics, manufacturers can perform comprehensive root cause analysis. This process involves
sifting through vast amounts of data to identify the underlying reasons for anomalies in yield. The insights derived from this
analysis can then be used to adjust manufacturing processes and prevent future defects, thereby improving wafer yield.
The Human Factor: Data Scientists and Engineers in Semiconductor Manufacturing
Moreover, big data analytics can also aid in the creation of detailed yield report in manufacturing. Yield reports are critical tools
for yield engineers, who are tasked with monitoring and improving yield in manufacturing. These reports provide comprehensive
information on the yield performance of different production batches, shifts, and equipment. By leveraging big data analytics,
these reports can be produced in real-time, allowing yield engineers to swiftly react to any issues and maintain optimal yield
levels.
However, deriving meaningful insights from big data in semiconductor manufacturing is not simply about having the right tools;
it's also about having the capability to manipulate and interpret the data effectively. This requires a team of skilled data scientists
and engineers who understand the manufacturing process and can develop sophisticated algorithms to extract valuable
information from the data.
4. Big Data Analytics in Financial Reconciliation and Product Planning
Another critical perspective in semiconductor manufacturing relates to financial reconciliation and product planning. One might
wonder, how does big data analytics assist in these areas? The answer lies in the depth and breadth of insights that can be
gleaned from this vast data.
In financial reconciliation, big-data analytics offers a granular view of production costs associated with each manufacturing stage
and product line. It can help identify areas where resources are being used inefficiently, contributing to increased costs. For
example, if the data analysis test reveals a high defect rate during a particular production stage, this could indicate a need for
process optimization or equipment maintenance to mitigate unnecessary expenses. By identifying and addressing these areas,
companies can significantly reduce costs and improve their bottom lines. When it comes to product planning, big-data analytics
offers even more invaluable insights. By analyzing the historical and real-time data from the production line, manufacturers can
predict future production needs with a high degree of accuracy. This predictive capability can help manufacturers better manage
their resources, avoid production bottlenecks, and ensure a steady supply of products to meet market demand.
Big Data Analytics in Supplier Evaluation and Executive Decision-making
Big data analytics can also be used to develop detailed product genealogies. A product genealogy is a record of all processes and
materials involved in producing a particular product, from raw materials to finished goods. It provides a clear picture of the
product's lifecycle, enabling manufacturers to trace the origin of any defects and take corrective measures. It also helps
manufacturers identify successful production processes and patterns that result in high-quality products, which can then be
replicated to enhance product quality and consistency. In addition to improving production and planning, big-data analytics can
significantly enhance purchasing decisions. Manufacturers often need to source materials from various suppliers, and the quality
of these materials can greatly impact the final product's quality. Big-data analytics can assist manufacturers in evaluating and
monitoring supplier performance, ensuring that only high-quality materials are used in production.
5. Finally, big-data analytics has an instrumental role in executive management. By providing a comprehensive view of operations, it helps executives
make strategic decisions about production, investment, and resource allocation. This could include decisions about adopting new technologies,
investing in equipment upgrades, or implementing new operational strategies.
Indeed, big-data analytics is redefining the semiconductor manufacturing industry. It provides unparalleled insights that drive operational
efficiency, enhance product quality, and improve profitability. As this field continues to evolve, it will undoubtedly become an even more integral
part of semiconductor manufacturing, enabling manufacturers to navigate the complexities of the industry and achieve their strategic objectives.
Conclusion
Big data analytics plays a vital role in semiconductor manufacturing. By leveraging big data analytics, semiconductor manufacturers can make
data-driven decisions that enhance product quality, improve yield, and streamline operations. However, to fully reap the benefits of big data
analytics, manufacturers need to invest in the right data analysis tools and cultivate the necessary talent to manipulate and interpret the data
effectively.
References:
1. Wang, S. F., Tseng, S. S., & Wang, W. C. (2016). Applying big-data techniques to improve yield rates and the manufacturing process. Journal
of Big Data, 3(1), 1-20.
2. Lin, C. T., Chiang, M. C., & Wen, C. H. (2017). Big data analytics for manufacturing Internet of Things: opportunities, challenges and
enabling technologies. Enterprise Information Systems, 11(4), 568-589.
3. Lu, Y., Morris, K. C., & Frechette, S. (2016). Current standards landscape for smart manufacturing systems. NISTIR, 8107.
4. Pham, V. (2018). Big data in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 31(3), 245-254.
5. Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance &
service innovation. Procedia CIRP, 38, 3-7.
6. Wang, L., & Wang, G. (2013). Big data in cyber-physical systems, digital manufacturing, and industry 4.0. International Journal of
Engineering and Manufacturing (IJEM), 3(5), 1-8.
7. HP Vertica. (2023). Data analytics solutions for the semiconductor industry.
8. Smith, K. (2023). The role of big data analytics in semiconductor manufacturing. Semiconductor Digest.