Fab-Wide Scheduling of Semiconductor Plants: A Large-Scale Industrial Deployment Case Study
This article draws from the contents of a paper presented at Winter Simulation Conference 2022, titled: “Fab-Wide Scheduling of Semiconductor Plants: A Large-Scale Industrial Deployment Case Study”.
An Introduction to Fab-Wide Scheduling
The semiconductor industry is one of the largest and most complex industries in the world. The critical factors in semiconductor manufacturing are the ability to rapidly develop and test novel technologies, improve manufacturing processes to reduce rework and waste, as well as meet production targets in terms of prescribed volumes and due dates. In this context, high quality scheduling is of paramount importance.
Due to the long cycle times, where a wafer is processed over a span of months, decision-making in semiconductor fabrication plants (fabs) is typically framed as a two-level problem. On one hand, global scheduling (or fab-wide) is tasked with the strategic management of factory assets while considering all work-in-progress, incoming and outgoing flows across the fab, expected resource availability and other constraints. On the other hand, local (or toolset-level) scheduling focuses on the operation of individual work centres. It is typically tasked with identifying the best immediate dispatch decisions i.e. which jobs waiting for dispatch should be assigned to which available machine.
Most development efforts to date have focused on the shorter time frame dispatch decisions i.e. local scheduling. This is a more manageable problem since there is little look-ahead and the scope is limited to a single or a few toolsets. Despite numerous research efforts, to date there has not been a published case study of a fab-wide scheduler successfully deployed in a large semiconductor manufacturing facility. Nevertheless, the potential for improvement at the fab-wide level is tremendous; there are numerous opportunities to improve throughout and have a step change in performance. For example:
- Bottlenecks occur due to repetition of process loops, high-cost machines with low capacity, and other physical or operational constraints. To manage them, a strategic approach is needed that looks at the bigger picture and avoids early dispatch of wafers that will end up in a bottleneck area.
- WIP flow control mechanisms (kanbans) are important for quality control but can block high-priority wafers. Fab-wide scheduling can greatly improve this aspect of operation.
- Timelinks (also known as timeloop, time lag, or qtime constraints) are challenging because they define the minimum or maximum amount of time between two or more consecutive process steps, leading to a conundrum of keeping downstream machines idle or not. Fab-wide scheduling can greatly assist by accurately predicting arrival times and deciding when to trigger timelinked lots.
Methodology
The scheduling framework proposed in this blog is hierarchical and consists of two main components which run independently and at different frequencies — the Toolset Scheduler (TS) and Fab-Wide Scheduler (FWS).
The Toolset Scheduler considers the currently in-process and/or upcoming process step of all wafers in the cluster.
FWS takes a view of the entire fab at once and considers multiple future steps for each wafer. It focuses on improving schedule quality by considering the flow of wafers through the fab, something the toolset scheduler cannot do due to its singlestep, toolset-level nature. The main purpose is to redirect flow through the fab and thereby improve flow linearity, reduce bottlenecks, improve WIP flow control management, and reduce timelink violations. Our FWS approach achieves this by predicting wait/cycle times for multiple future steps, analysing those predicted wait/cycle times with respect to the different areas of potential improvement, and re-prioritising wafer steps in a way that guarantees improved (weighted) cycle times. In brief, FWS combines two main elements: (i) an operational module that captures in full detail all relevant constraints e.g. detailed process time modelling, machine maintenance, shift changes, dynamic batching constraints, kanbans etc. (ii) a search module that identifies beneficial priority changes given the evolving fab conditions and state features.
FWS communicates with the toolset schedulers via priority weights (and some other predicted timing information) for individual steps of a wafer, as shown in Figure 2. An advantage of our approach is that, while FWS always schedules all tools in the fab, users can specify which toolsets are subject to guidance; FWS adjusts its search accordingly. This is particularly useful for gradually rolling out FWS in a fab and evaluating its impact. In addition, the guidance strength is controllable - although full guidance is the optimal choice, tuning down guidance allows for a more gradual deployment.
Seagate Deployment
Seagate is a world leader in data storage technology, with more than 40% share of the global Hard Disk Drive (HDD) market. The Springtown facility in Northern Ireland produces around 25% of the total global demand for recording heads, the critical component in a HDD. Flexciton’s FWS / TS scheduling system was trialled in Seagate Springtown between March-May 2022. After successful testing, the system has been operational 24/7 since June 2022; a timeline is shown in Figure 3.
It is important to note that deploying and testing a novel piece of technology in a large factory that runs around the clock presents many practical challenges to be overcome:
- Controllability (scope): important to ensure that the new development is deployed in a controlled manner. The FWS-TS guidance scheme allows for localised trials, where focus can be placed on problematic areas and gradually increase scope.
- Controllability (magnitude): it is useful to only focus on cases with obvious merit first. This is achieved by controlling guidance strength.
- Explainability: important to be able to detect and reason about the changes. This is achieved by a combination of UI features and support tools which have been designed to give operators and managers situational awareness.
Results and Learnings
Quantifying the benefit of an alternative scheduling approach remains a challenging task. When deployed in a real plant, traditional A/B testing between pre and post-deployment suffer from (i) dynamic fab conditions (ii) an ever-changing product mix and (iii) evolving capabilities of the fab e.g. increased/decreased labour capacity and new tool commissioning/decommissioning.
As such, it was decided to look at the impact from different angles - a statistically significant impact would be expected to result in a substantial shift in numerous business processes and metrics. In particular, three different aspects were examined.
- Deep dives on specific toolsets and metrics.
- Comparison against internal simulation and planning tools.
- Observing the impact on manual interventions.
Notably, all three approaches indicated a change in fab performance between pre and post-deployment; more details will be shared in future articles. In the Winter Sim Conference paper presented in December 2022, we focused on the latter point; A proxy we can use for this benefit is the volume of ad hoc control flow rules activated/deactivated in the fab. Every day, specialists have to define numerous, in some cases even hundreds, of ad hoc control flow rules to better manage operations given the prevalent conditions. For example, setting a ”hard down” rule, where lots are manually placed on hold so as not to continue to a downstream bottleneck. In Figure 5, we show the number of ad hoc operational rules implemented in the Seagate Springtown fab between weeks 2 and 26 of the year 2022 (i.e. from early January until late June). As can be seen in the final weeks, the number of ad hoc rule transactions averaged less than 150 per week, a decrease of over 300% compared to the pre-deployment period. This is strong evidence that FWS deployment reduced massively manual interventions required to effectively control flows within the fab.
Conclusions
The main takeaway of the Winter Sim paper is that the increased horizon look-ahead and global nature of FWS presents numerous opportunities for a step change in factory KPIs. The Flexciton FWS was successfully trialled at Seagate Springtown over 3 months in 2022 and has been fully enabled across the fab since June 2022. It resulted in a radical decrease of interventions previously used to manually control wafer flows. Further analysis suggests that Flexciton’s TS and FWS schedulers have achieved substantial improvements in throughput and cycle times.
Author: Ioannis Konstantelos, Principal Engineer
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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 Flexciton.
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.
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
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.
Innovate UK, part of UK Research and Innovation, has invested in Flexciton and Seagate Technology's production planning project to help improve UK semiconductor manufacturing.
London, UK – 1 Oct – Flexciton, a UK-based software company at the forefront of autonomous semiconductor manufacturing solutions, is excited to announce investment from Innovate UK in a strategic collaboration with Seagate Technology’s Northern Ireland facility. Innovate UK, the UK’s innovation agency, drives productivity and economic growth by supporting businesses to develop and realize the potential of new ideas. As part of their £11.5 million investment across 16 pioneering projects, this collaboration will help develop and demonstrate cutting-edge technology to boost semiconductor manufacturing efficiency and enhance the UK’s role in the global semiconductor supply chain.
Jamie Potter, CEO and Cofounder of Flexciton, commented:
"We are thrilled to partner with Seagate Technology to bring yet another Flexciton innovation to market. By combining our autonomous scheduling system with Flex Planner, we are enhancing productivity in semiconductor wafer facilities and driving greater adoption of autonomous manufacturing."
The partnership aligns directly with the UK government’s National Semiconductor Strategy, which seeks to secure the UK’s position as a key player in the global semiconductor industry. Flexciton’s contribution to this strategy is not just a testament to its cutting-edge technology but also highlights the company’s role in reinforcing supply chain resilience and scaling up manufacturing capabilities within the UK.
Flex Planner: A breakthrough solution for chip manufacturing
At the heart of this project is Flex Planner, the first closed-loop production planning solution for semiconductor manufacturing with the ability to control the flow of WIP in a fab over the next 2-4 weeks, autonomously avoiding dynamic bottlenecks, reducing cycle times, and improving on-time delivery performance.
Supporting the UK's semiconductor growth
The UK government’s investment in semiconductor innovation underlines its commitment to fostering cutting-edge solutions that bolster the sector’s growth. The semiconductor industry is projected to grow from £10 billion to £17 billion by 2030, with initiatives like this collaboration driving the innovation necessary to achieve these goals.
Flexciton’s partnership with Seagate exemplifies how collaboration between technology innovators and manufacturers can lead to transformative advances in the industry. The funding from Innovate UK enables both companies to develop and test solutions that not only enhance productivity but also position the UK as a critical link in the global semiconductor ecosystem.
About Flexciton
Flexciton is pioneering autonomous technology for production scheduling and planning in semiconductor manufacturing. Leveraging advanced AI and optimization technology, we tackle the increasing complexity of chipmaking processes. By simplifying and streamlining wafer fabrication with our next-generation solutions, we enable semiconductor fabs to significantly enhance efficiency, boost productivity, and reduce costs. Empowering manufacturers with unmatched precision and agility, Flexciton is revolutionizing wafer fabrication to meet the demands of modern semiconductor production.
For media inquiries, please contact: media@flexciton.com
The semiconductor industry is set to receive $1tn in investment over the next six years, driven by AI and advanced technologies, with over 100 new wafer fabs expected. However, labor shortages continue to pose a challenge, pushing the need for autonomous wafer fabs to ensure continued growth.
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.
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.
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.
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.