The semiconductor industry's journey toward fully autonomous manufacturing is underway, driven by advanced technologies and strategic investment. Staying ahead in smart manufacturing technologies has become paramount for global competitiveness. This topic was the focal point of the recent panel discussion webinar, hosted by Jamie Potter, Flexction CEO & Cofounder. The panel featured industry leaders representing fabs and suppliers: Matthew Johnson, VP of Wafer Fab Operations at Seagate; Patrick Sorenson, Industrial Engineer at Microchip Technology; Francisco Lobo, CEO of Critical Manufacturing; and Madhav Kidambi, Technical Marketing Director at Applied Materials.‍

Survey Insights: Where Are We Now?

The panel discussion was initiated with a presentation of the findings from Flexciton's inaugural Front End Manufacturing Insights survey, conducted among fabs in the US, Europe, and Asia. Key takeaways included:

• A majority of respondents see autonomous manufacturing as achievable within the next decade.
• Data standardization and integration remain major barriers, delaying scalable solutions.
• Cloud computing, IoT and Mathematical Optimization stand as the top three advanced technologies that fabs have adopted so far. 

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These insights laid a strong foundation for a lively discussion, highlighting the shared vision while addressing divergent strategies and challenges.

Insights from Industry Experts

Pragmatism Over Perfection in Data Models

Francisco Lobo emphasized the importance of starting with what’s available when building scalable solutions.

“Instead of building a complete model from scratch, leverage existing standards and your MES infrastructure. Begin with a pragmatic approach and evolve as you learn.”

This iterative strategy ensures companies can start deriving value early, without waiting years for a perfect model to be developed.

Strategic Investments In Downturns

While many fabs postpone investments during downcycles, Matthew Johnson emphasizes that smart manufacturing investments should be continuous rather than cyclical. He highlighted the strategic advantage of such approach:

“In down cycles, you often need these solutions the most. For example, using smart manufacturing to scale metrology tools through sampling can significantly stretch your existing resources without capital-heavy investments.”

His insight underscores how downturns provide a window to refine processes for long-term gains.

Getting Leadership Buy-in

Securing leadership support for smart manufacturing investments remains challenging when benefits aren't immediately apparent. Patrick Sorenson shares that the ROI justification was easier during the recent upcycle:

"If we just get a few more lots out of the fab when we have more demand than capacity, that will pay for itself."

In other scenarios, focus on demonstrating benefits through yield improvements, capital avoidance, or labor efficiency.

Industry Alignment on the Vision

Madhav Kidambi observed a growing consensus around the end goal of autonomous manufacturing, even as companies differ in their pathways:

“The vision of Lights Out manufacturing is clear, but strategies are evolving as companies learn how to justify and sequence investments to sustain the journey.”

Ecosystem Collaboration and The Path Towards Autonomy

A key theme emerging from the discussion is the importance of collaboration between suppliers and fabs. This includes:

• Open platforms and integration capabilities
• Standardized data protocols
• Partner ecosystems for specialized solutions
• Shared innovation initiatives

As the industry progresses toward autonomous manufacturing, success will depend on:

• Maintaining continuous investment in smart technologies
• Taking pragmatic approaches to data integration
• Developing clear ROI frameworks
• Fostering collaboration across the ecosystem
• Building upon existing systems and standards

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As Matt from Seagate concludes,

"Fab operation is really a journey of continuous improvement, and the pursuit of smart technologies is a fundamental tenet of our strategy to ensure that we meet the objectives as an organization."

Watch the Full Webinar

The conversation is packed with actionable insights on overcoming barriers, achieving quick wins, and navigating the complexities of smart manufacturing adoption. Don’t miss out—click here to watch the full discussion recording.

Over the next 6 years, the semiconductor industry is set to receive around $1tn in investment. The opportunities for growth – driven by the rapid rise of AI, autonomous and electric vehicles, and high-performance computing – are enormous. To support this anticipated growth, over 100 new wafer fabs are expected to emerge worldwide in the coming years (Ajit Manocha, SEMI 2024).

However, a significant challenge looms: labor. In the US, one-third of semiconductor workers are now aged 55 or older. Younger generations are increasingly drawn to giants like Google, Apple and Meta for their exciting technological innovation and brand prestige, making it difficult for semiconductor employers to compete. In recent years, the likelihood of employees leaving their jobs in the semiconductor sector has risen by 13% (McKinsey, 2024).

To operate these new fabs effectively, the industry must find a solution. The Autonomous Wafer Fab, a self-optimizing facility with minimal human intervention and seamless production, is looking increasingly likely to be the solution chipmakers need. This vision, long held by the industry, now needs to be accelerated due to current labor pressures.

Thankfully, rapid advancements in artificial intelligence (AI) and Internet of Things (IoT) mean that the Autonomous Wafer Fab is no longer a distant dream but an attainable goal. In this blog, we will explore what an Autonomous Wafer Fab will look like, how we can achieve this milestone, the expected outcomes, and the timeline for reaching this transformative state.

What will an Autonomous Wafer Fab look like?

Imagine a wafer fab where the entire production process is seamlessly interconnected and self-regulating, free to make decisions on its own. In this autonomous environment, advanced algorithms, IoT, AI and optimization technologies work in harmony to optimize every aspect of the manufacturing process. From daily manufacturing decisions to product quality control and fault prediction, every step is meticulously coordinated without the need for human intervention.

Key features of an Autonomous Wafer Fab:

Intelligent Scheduling and Planning: The heart of the autonomous fab lies in its scheduling and planning capabilities. By leveraging advancements such as Autonomous Scheduling Technology (AST), the fab has the power to exhaustively evaluate billions of potential scenarios and guarantee the optimal course for production. This ensures that all constraints and variables are considered, leading to superior outcomes in terms of throughput, cycle time, and on-time delivery.

Real-Time Adaptability: An autonomous fab is equipped with sensors and IoT devices that continuously monitor the production environment. These devices can feed real-time data into the scheduling system, allowing it to dynamically adjust schedules and production plans in response to any changes or disruptions. 

Digital Twin: Digital Twin technology mirrors real-time operations through storing masses of data from sensors and IoT devices. This standardized data schema allows for rapid introduction of new technologies and better scalability. Moreover, by simulating production processes, it helps to model possible scenarios – such as KPI adjustments – within the specific constraints of the fab.

Predictive maintenance: Predictive maintenance systems will anticipate equipment failures before they occur, reducing downtime and extending the lifespan of critical machinery. This proactive approach ensures that the fab operates at peak efficiency with minimal interruptions. Robotics will carry out the physical maintenance tasks identified by these systems, and when human intervention is necessary, remote maintenance capabilities will allow technicians to diagnose and address issues without being on-site.

The Control Room: In an autonomous fab, decision-making is driven by data and algorithms. The interconnected system can balance trade-offs between competing objectives, such as maximizing throughput while minimizing cycle time, with unparalleled precision. That said, critical decisions such as overall fab objectives may still be left to humans in the “control room”, who could be on the fab site or 9000 km away… 

How can we get there?

Achieving the vision of an Autonomous Wafer Fab requires a multi-faceted approach that integrates technological innovation, strategic investments, and a cultural shift towards embracing automation. Here are the key steps to pave the way:

A Robust Roadmap: All fabs within an organization need to have a common vision. Key milestones need to be laid out to help navigate each fab through the transition with clear actions at each stage. SEMI’s smart manufacturing roadmap offers an insight into what this could look like.  

Investing in Novel Technologies: The pivotal step towards autonomy is investing in the latest technologies, including AI, machine learning, AST, and IoT. These technologies form the backbone of the autonomous fab, enabling intelligent planning and scheduling, real-time monitoring, and adaptive control.

Data Integration and Analytics: A crucial aspect of autonomy is the seamless integration of data from various sources within the fab. By harnessing big data analytics, fabs can not only gain deep insights into their operations, but they will have the correct data in place to support autonomous systems further down the line. 

Developing Skilled Workforce: While the goal is to minimize human intervention, the semiconductor industry will still require skilled professionals who can manage and maintain advanced systems. Investing in workforce training and development to fill the current void is essential to ensure a smooth transition.

Collaborative Ecosystem: Even the biggest of chipmakers is unlikely to reach the autonomous fab all on their own. Collaboration with technology providers, research institutions, and industry partners will be key. Sharing knowledge and best practices can accelerate the development and deployment of autonomous solutions.

Pilot Programs and Gradual Implementation: Transitioning to an autonomous fab should be approached incrementally. Starting with pilot programs to test and refine technologies in a controlled environment will help identify challenges and demonstrate the benefits. Gradual implementation allows for continuous improvement and adaptation.

How will fabs benefit? 

The transition to an Autonomous Wafer Fab promises a multitude of benefits that will revolutionize semiconductor manufacturing:

Enhanced Efficiency: By optimizing production schedules and processes, autonomous fabs will achieve higher throughput and better resource utilization. This translates to increased production capacity and reduced operational costs.

Better Quality: Advanced process control and real-time adaptability ensure consistent product quality, minimizing defects and rework. This leads to higher yields and greater customer satisfaction.

Reduced Downtime: Predictive maintenance and automated decision-making reduce equipment failures and production interruptions. This results in higher uptime and more reliable operations.

Improved Flexibility: Autonomous fabs can quickly adapt to changing market demands and production requirements. This flexibility enables manufacturers to respond rapidly to customer needs and stay competitive in a dynamic industry.

Cost Savings: The efficiencies gained from autonomous operations lead to significant cost savings. Reduced labor intensity, lower material waste, and optimized energy consumption contribute to a more cost-effective production process.

Sounds great, but when will it become a reality?

The journey towards an Autonomous Wafer Fab is well underway, but the timeline for full realization varies depending on several factors, including technological advancements, industry adoption, and investment levels. However, significant progress is expected within the next decade.

Short-Term (1-3 Years):

• Implementation of pilot programs and continual adoption of AI, IoT, AST and other advanced technologies.
• Incremental improvements in scheduling, process control, and maintenance practices.
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Medium-Term (3-7 Years):

• Broader adoption of autonomous solutions across the industry.
• Enhanced data integration and analytics capabilities.
• Development of a skilled workforce to support autonomous operations.
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Long-Term (7-10 Years and Beyond):

• Full realization of the Autonomous Wafer Fab with minimal human intervention.
• Industry-wide standards and best practices for autonomous manufacturing.
• Continuous innovation and refinement of autonomous technologies.‍

Conclusion

The pathway to the Autonomous Wafer Fab is a transformative journey that holds immense potential for the semiconductor industry. By embracing advanced technologies, fostering collaboration, and investing in the future workforce, fabs can unlock unprecedented levels of efficiency, quality, and flexibility. Autonomous Scheduling Technology, as a key pillar, will play a crucial role in this evolution, driving the industry towards a future where production is seamless, self-optimizing, and truly autonomous. The vision of an Autonomous Wafer Fab is not just a distant possibility but an imminent reality, poised to redefine the landscape of semiconductor manufacturing.
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Now available to download: our new Autonomous Scheduling Technology White Paper

We have just released a new White Paper on Autonomous Scheduling Technology (AST) with insights into the latest advancements and benefits.

Click here to read it.

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In the fast-paced world of semiconductor manufacturing, efficient production scheduling is crucial for chipmakers to maintain competitiveness and profitability. The scheduling methods used in wafer fabs can be classified into two main categories: heuristics and mathematical optimization. Both methods aim to achieve the same goal: to provide the best schedules within their capabilities. However, because they utilize different problem-solving methodologies, the outcome is dramatically different. Simply put, heuristics generates solutions by making decisions based on if-then rules predefined by a human, while optimization algorithms search through billions of possible scenarios to automatically select the most optimal one. 

Autonomous Scheduling Technology (AST) features mathematical optimization combined with smart decomposition, allowing the quick delivery of optimal production schedules. Whether you are a fab manager or industrial engineer, the experience and results of applying Autonomous Scheduling in your fab are fundamentally different compared to a heuristic scheduler.  

Here's how switching to AST can impact your fab.

Consistent and Superior KPIs Guaranteed

Autonomous Scheduling Technology (AST) evaluates all constraints and variables in the production process simultaneously, ensuring optimal decision-making. Unlike heuristics schedulers, which require ongoing trial and error with if-then rules to solve the problem, AST allows the user to balance trade-offs between high level fab objectives. With its forward-looking capability, it can assess the consequences of scheduling decisions across the entire production horizon and generate schedules that guarantee that the fab's global objectives are met. The tests we have conducted against a heuristic-based scheduler have proven that Autonomous Scheduling delivered superior results. Book a demo to find out more.

Never miss a shipment

One of the most critical aspects of fab operations is meeting On-Time-Delivery deadlines. With AST, schedules are optimized towards specific fab objectives, ensuring that production targets align with business goals. Mark Patton, Director of Manufacturing Seagate Springtown, confirmed that adopting Autonomous Scheduling in his fab allowed him to:

"improve our predictability of delivery by meeting weekly customer commits. With a lengthy cycle time build, this predictability and linearity has been key to enabling the successful delivery and execution of meeting commits consistently."

Reduced workload (by at least 50%)

The reactive nature of heuristic-based schedulers places a significant burden on industrial engineers, who must constantly – and manually – tune rules and adjust parameters. To ensure these systems run optimally, fab managers must dedicate at least one industrial engineer to working full-time on maintaining them. With AST, the workload is significantly reduced due to the system's ability to optimize schedules autonomously (without human intervention). This means there will be no more firefighting when the WIP profile changes. This reduction in labor intensity frees up engineers to engage in value-added activities.

Reduced rework, improved yield

Some areas of a fab are notoriously challenging to optimize. For example, the diffusion and clean area is home to very complex time constraints, also known as timelinks. When timelinks are violated, wafers either require rework or must be scrapped. Either way, it's a considerable cost for a fab. Autonomous Scheduling Technology is highly effective at managing conflicting KPIs with its multi-objective optimization capabilities. AST dynamically adjusts to changes in the fabrication process to consistently eliminate timelink violations whilst maximizing throughput.  

Confidence in Balancing Trade-offs

With its ability to look ahead, Autonomous Scheduling Technology can predict the consequences of different trade-off settings. This capability is particularly valuable when balancing competing objectives like throughput and cycle time. Users of legacy schedulers would typically move sliders to adjust the settings and wait a considerable amount of time to assess whether the adjustments generate the desired scheduling behavior. If not, further iterations are required, and the process repeats. In contrast, AST can evaluate billions of potential scenarios and determine the optimal balance between conflicting goals. For example, it can predict the exact impact of prioritizing larger batches over shorter cycle times, allowing fab managers to make informed decisions with confidence. This strategic foresight ensures that the best possible trade-offs are made, optimizing the whole fab to meet overarching objectives. 

Conclusion

In an industry where efficiency and precision are paramount, Autonomous Scheduling Technology provides a distinct competitive advantage. It equips fabs with the tools to consistently outperform legacy systems, streamline operations, and ultimately drive greater profitability. By investing today in upgrading their legacy scheduling systems to Autonomous Scheduling Technology, wafer fabs are not only optimizing their current operations but also taking an important step toward the autonomous fab of the future.

Now available to download: our new Autonomous Scheduling Technology White Paper

We have just released a new White Paper on Autonomous Scheduling Technology (AST) with insights into the latest advancements and benefits.

‍Click here to read it. 

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