Why Optimized Scheduling is the Answer to Balancing Reticle Moves and Cycle Time
Introduction
Photolithography processes are central to producing computer chips and semiconductor devices. However, they are typically considered to be bottlenecks due to their reliance on a critical secondary asset; reticles. Reticles are limited in number and yet are a critical piece of the coat-expose-develop loop. What is more, reticles are delicate in nature; they are enclosed in purpose-built cases for their transport in order to keep the potential of damage or distortion to a minimum.
As such, a fundamental tradeoff arises when operating photolithography toolsets: moving a reticle to the machine it is needed most (to carry out high-priority tasks) clashes with the requirement to be conservative with its transport. In theory, there are several compromises that the operator can make to reduce reticle movements - waiting a bit longer to ensure more wafers arrive to a machine and a larger batch can be processed with a single move is one example. However, in practice, identifying these strategic actions and balancing between the competing goals is highly complex. Flexciton can provide a solution to this issue by leveraging the power and flexibility of optimisation.
In this article, we show how the Flexciton’s scheduling engine can balance between minimising cycle times and reticle moves. Through a series of example case studies, we delve into the scheduling trade-offs that arise in the day-to-day operation of a semiconductor fab and how Flexciton’s solution can assist in uncovering schedules that optimally balance across competing goals.
Trade-off frontier with the Flexciton scheduler
The Flexciton scheduler can accommodate a range of user-defined objectives. The fab operator is typically interested in minimising KPIs such as cycle times but may also want to include other considerations, such as penalisation of labour-intensive decisions e.g. number of batches built. In this vein, we have recently introduced a new component; the number of reticle moves carried out. As shown in Figure 1, the user is able to define a penalty factor for reticle moves; the higher the value, the harder the engine will try to avoid moving reticles.
In the example case study we have 6 machines and 48 wafers to be scheduled, with a total of 4 reticles.
Reticle 10001 is required for all lots schedulable on machines 01, 02 and 03. Deciding on how to move this in-demand reticle across the 3 machines will impact both the number of moves as well as cycle times, particularly as there are some high-priority wafers waiting to be dispatched. Reticle 10001 is originally loaded in machine 01.
The other three reticles, 20001, 20002 and 20003 are initially loaded on machines 04, 05 and 06 and can be used by all three toolsets interchangeably. However, different machines are better suited to different reticles; for example in our case study, the same process is completed faster if using reticle 20001 on machine 06. Note that all machines have a maximum batch size of 4 wafers.
Case Study 1: Focusing solely on cycle times
We start off by not penalising the number of reticle moves and solely minimising the total priority-weighted cycle times (TWCT) across all wafers. The optimal schedule produced by Flexciton’s engine is shown in Figure 2.
There are a total of 7 reticle moves, noted with red arrows in the figure below. 4 moves pertain to reticle 10001 which is moved from its initial location 01 to 03 and then 02 to carry out some high-priority wafers (as evidenced by the circled 1/2/3 next to the job names). The reticle is then moved out again to machine 01 and finally to machine 03 to carry out some lower priority jobs. Looking at machines 04, 05 and 06, the engine decides to immediately swap the reticles between the machines, to ensure that each lot is fed to its most suitable (in terms of processing times and capability) machine. The TWCT of all 48 wafer steps (where priority weights are user-defined and in this case range from 1 for highest-priority to 0.1 for lowest-priority wafers) is 16.79 hours.
Case Study 2: Moderate penalisation of reticle moves
In this second study, we have penalised reticle moves; the ratio for balancing TWCT and reticle moves has been set to 100:75 i.e. we will choose to avoid a reticle move only if its avoidance translates to an increase of TWCT of 0.75 hours or less. This is quite relaxed, but is aimed at avoiding reticle moves with little benefit, since the risk of potential damage is deemed higher. The optimal schedule obtained is shown in Figure 3.
In this study, there are a total of 5 reticle moves, noted with red arrows in the figure below. 3 moves pertain to reticle 10001 and its journey across machines 01, 02 and 03. The main difference to the previous scheduling pattern is that now we do not move the reticle back to machine 03 to carry out the very last batch of low priority wafers. Instead, we choose to wait for their arrival and carry them right after the high-priority batch finishes a bit after 19:00. This way we avoid that final reticle move, while also incurring a delay in the high-priority wafers scheduled on machine 02 which now have to be moved from 19:15 (in study 1) to 19:30.
Looking at machines 04, 05 and 06, the engine decides to immediately swap only two the three reticles this time, and leave reticle 20001 on its initial machine. Although that initial setup is not ideal in terms of processing times it does prevent the reticle move deemed to be “lower value”. The TWCT of all 48 wafer steps is 18.73 hours.
Case Study 3: Only necessary reticle moves
In this study we look at the extreme case of using a very high penalty on reticle moves, hence allowing only absolutely necessary reticle moves. In particular, we have opted to use a TWCT to reticle move cost ratio of 1:10. In such cases, the operator is willing to accept sub-optimal job-machine allocation decisions, as well as delayed scheduling of high-priority wafers, for the purpose of keeping reticle movement to the absolute minimum. The optimal schedule obtained is shown in Figure 4.
In this study, the total number of reticle moves has come down to just 2 moves, noted with red arrows in the figure below. Both moves pertain to reticle 10001 and its journey across machines 01, 02 and 03 to ensure all wafers are completed. In the case of machines 04, 05 and 06, we are still able to carry out all tasks, albeit with longer processing times, as evidenced in the much later finishing times of the machines. The TWCT of all 48 wafer steps is 23.20 hours.
Exploring the trade-off frontier
Plotting the aforementioned runs (and also some more data points), we obtain Figure 5, which clearly illustrates the trade-off at play here. As we traverse the penalty factor from a low to a high value, the number of reticle moves drops and the cycle times increase. As expected, these relationships are monotonic but not smooth, since they depend on discrete events. Note also that both curves are bounded both from above and below, corresponding to the absolute minimum number of reticle moves required (in this case 2) and the absolute maximum number of reticle moves that is optimal (in this case 7).
By running a few scenarios with different parameters, the Flexciton engine opens up the possibility to explore the tradeoff frontier in detail, enabling operators to quantify how KPIs would change with a more relaxed or constrained attitude towards reticle movements.
Performance in real-world applications
In practice, enabling the Flexciton scheduling engine to consider reticle moves is a computationally challenging task, involving novel development in the model’s MILP formulations and heuristics. Nevertheless, this feature has been accommodated with no deterioration to performance and schedule quality. The Flexciton engine is capable of scheduling thousands of wafers across hundreds of machines in a few minutes while also controlling for the operator’s tolerance to reticle movement.
Indicatively, we showcase results obtained from scheduling a real-world fab plant. At the time of the study, the plant had a total of 3,478 wafers to be scheduled on 209 toolsets (with a total of 358 load ports). We computed two schedules: one with low and one with high penalisation of reticle moves. These scheduling runs were computed in roughly the same time: respectively, confirming that despite the added complexity, this feature can scale well and provide a schedule in a few minutes.
Focusing on the reticle machines, the results obtained suggested that reticle movements could be reduced by around 26% while leading to an increase in total cycle times of around 2%. Note that these results are priority-weighted, with further analysis revealing that high-priority wafers are not substantially impacted; the optimiser is able to identify “low-value” reticle movements relating to e.g. early processing of a low priority wafer and either avoid that movement by using an alternative recipe, or deferring that movement to later when a low-priority wafer can be combined with a high-priority wafer in a batch.
Conclusions
Reticle scheduling is a very important consideration in the scheduling of advanced semiconductor fabrication plants. This resource, already highly constrained, comes with a critical consideration in practice: frequent movements and manual handling of the delicate reticles increase the risk of damage or distortion during transport. As such, the number of times a reticle is moved to a new machine must be managed conservatively. This inadvertently clashes with the operator’s fundamental objective of reducing cycle times.
Flexciton has extended the capabilities of our Mixed Integer Linear Programming (MILP) scheduling engine to natively accommodate the modelling and penalisation of reticle movements. This allows the user to define their own risk profile, so as to limit reticle movements solely to cases deemed of high value. In addition, the engine opens up the possibility to explore this tradeoff frontier in detail, enabling operators to quantify how their plant’s performance may change with a more relaxed or constrained attitude towards reticle movements.
Authors
Ioannis Konstantelos is a Principal Optimisation Engineer at Flexciton. He holds a PhD from Imperial College London and has published over 50 conference and journal papers on optimization and artificial intelligence methods. Ioannis joined Flexciton over 3 years ago and is involved in the development of Flexciton’s scheduling engine.
Charles Thomas is a Test Analyst with a background in Mechanical Engineering and a Masters degree from the University of Southampton. He has been at Flexciton for 2 years and leads the benchmarking and testing of the application with a particular focus on scheduling engine performance.
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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.