The Reticle Allocation Problem and How to Approach it [Tech Paper Review]
The photolithography process is considered the most critical step in semiconductor wafer fabrication, where geometric shapes and patterns are reproduced onto a silicon wafer, ultimately creating the integrated circuits.
What makes this process unique is the use of additional resources, called reticles. The reticle is a photomask used to expose ultraviolet radiation that generates a specific pattern into the wafer. When not in use, reticles are stored in dedicated storage with a fixed capacity, called a stocker. Although the problem of allocating reticles in a stocker is not a “core” one in wafer production scheduling, if not optimized, it might significantly impact overall production efficiency by causing bottlenecks.
This week, Daniel Cifuentes Daza, one of the Optimization Engineers here at Flexciton, explores this problem by reviewing a technical paper by Benzoni, A. et. al. – “Allocating reticles in an automated stocker for semiconductor manufacturing facility” – and contrasting their approach with the one we use when scheduling at Flexciton.
The reticle and stocker bottleneck:
A fab working with a wide variety of products may need several thousand reticles at any given time to fulfil their production requirements [2]. Not only must reticles be stored in a stocker, as explained above, but they often need to be transported in a container known as a pod in order to prevent contamination too. Therefore, the capacity and availability of stockers and pods within the fab makes deciding where each reticle should be stored at each step of the production schedule extremely complex – frequently causing bottlenecks [3].
To manage this process, fabs need to decide the best way to allocate reticles into pods, and then try to find an optimal assignment between pods and tools. However, there is another optimization problem at hand that complicates the process further; the position of reticles within the limited-capacity compartments of the stocker itself.
The time to retrieve a reticle from storage can be drastically different depending on its own location inside the stocker, thus leading to large inconsistencies in the so-called processing time of the stocker. As a result, the stocker can become a bottleneck by not dispensing reticles fast enough to meet wafer demand. Therefore, the reticle allocation problem also consists of choosing which reticles are to be stored in the low-capacity fast-retrieval compartment (“retpod”) vs the high-capacity slow-retrieval compartment.
In order to explore what might be the best way to address this problem, we have reviewed a tech paper published by IEEE for the WSC conference in 2020. The authors of the paper address the allocation issue using the famous knapsack problem. This approach will be evaluated in the next section of this article – with the pros and cons being discussed – before discussing how the proposed solution compares to how we model photolithography tools here at Flexciton.
The reviewed paper approach:
In “Allocating reticles in an automated stocker for semiconductor manufacturing facility” by Benzoni, A. et. al. (2020) [5] the stockers examined by the authors have two compartments – one where reticles are stored using pods; retpod, and another where they do not use pods. The main objective is to allocate reticles into the retpod compartment, as this has faster retrieval times.
Additionally, the authors consider:
(1) the reticles, as the main resource of the problem,
(2) the steps where wafers will need to use the reticles in the near future, and
(3) the capacitated storage for reticles; the compartments of the stockers. Additionally, they see each reticle as having a profit value; the number of wafers processed in the batch. With this initial information, the problem can be modelled as the well-known knapsack problem.
Sheveleva, A. et. al (2021) [4] defines the knapsack problem as the following:
“There are k items with weight nk and value ck, and a knapsack with a capacity N. The problem is to fill the knapsack with items with the maximum total value, respecting the knapsack’s capacity limit”
In this case, each k item is a reticle, where its corresponding weight is always 1, and its value ck refers to the profit value. The knapsack is the retpod compartment of the stocker.
The knapsack problem is an NP-hard combinatorial problem that has been studied for many years within computer science, operations research, and other sciences. Therefore, due to its complexity, the authors decided to use a well-known heuristic. Here, the approach is to rank each reticle according to a specified objective value ratio and then fill the knapsack with the first N elements fulfilling its capacity.
The authors benchmarked three different objective functions for this heuristic as follows:
- Reticles are ranked according to their priority and their probability of being used soon. Reticles required by wafers in the short term have higher priority than those needed in wafers for later steps.
- Similar to (1), but the ranking also considers the total number of wafers that each reticle is being used by.
- Ranks reticles by the priority and probability of the reticle to be used, but only considers the most immediate reticles being used by at least one wafer.
The three approaches reported an increase in the utilisation of the reticles from around 8% to 20%. This implementation also led to a reduction of processing times for the stockers of 1 hour. Strategies 1 and 2 showed the lowest error percentage, which is expected as Strategy 3 does not consider future steps where reticles are used.
Flexciton’s view:
Using the knapsack approach to solve this problem certainly has some positive points. Firstly, using a heuristic method is easy to implement and does not require much computational time, which also makes it scalable to industrial-sized problems. Secondly, it is trivial to work out why certain allocation decisions are taken, making it highly understandable. Lastly, the approach is flexible because the user can modify the objective function of the heuristic depending on the fab’s goals.
However, the issue of reticle allocation is just a small piece of the complex wafer manufacturing process. Since this approach is modelled as a standalone problem, it is creating feasible solutions for the reticle stocker alone without considering the state of the rest of the fab. This will likely lead to inconsistencies as the wafer schedule is intrinsically linked to the reticle allocation.
In addition, the approach described in the paper models a simplification of the photolithography area. There is relevant information missing, such as the availability of pods in the fab and their possible allocation to machines, transfer times, and load and unload times. The use of this information would give robustness to the approach.
Flexciton’s way:
At Flexciton, we consider that the best way to tackle the reticle allocation problem is to proactively generate not only feasible solutions, but optimized production schedules. In order to do this, we take into account all the scheduling constraints for reticles available within our optimization engine – using information such as:
- Availability and capacity of pods for reticles
- The scheduling of pod usage – taking into account the number of pod ports in the machine
- Storage of pods into stockers, considering its capacity
- Transition of pods between stocker and machine
- Load, and unload times for reticles into pods and pods into machines
- Variable stocker unload time on account of where the reticles are located
- The reticles and pods already loaded into the machines; where they are in the fab at the time of scheduling
The benefit of considering a multitude of information like this all in one optimization model means that we can provide a consistent and robust production schedule that takes into account all the constraints of reticles, pods and stockers. Additionally, our scheduler allows the user to configure their specific business objectives into the optimization process in order to meet their fab’s KPIs – the algorithm is then calculated and an optimized schedule is returned in a matter of minutes. All of this means that our technology is able to return a reliable, scalable and flexible solution that is tailored to our client’s needs – whilst optimizing the photolithography area in its entirety.
References:
[1] Y. T. Lin, C. C. Hsu and S. Tseng, "A Semiconductor Photolithography Overlay Analysis System Using Image Processing Approach," Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007), 2007, pp. 63-69, doi: 10.1109/ISM.Workshops.2007.16.
[2] S. L. M. de Diaz, J. W. Fowler, M. E. Pfund, G. T. Mackulak and M. Hickie, "Evaluating the impacts of reticle requirements in semiconductor wafer fabrication," in IEEE Transactions on Semiconductor Manufacturing, vol. 18, no. 4, pp. 622-632, Nov. 2005, doi: 10.1109/TSM.2005.858502.
[3] You-Jin Park and Ha-Ran Hwang, "A rule-based simulation approach to scheduling problem in semiconductor photolithography process," 2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA), 2013, pp. 1-4, doi: 10.1109/SITA.2013.6560788.
[4] A. M. Sheveleva and S. A. Belyaev, "Development of the Software for Solving the Knapsack Problem by Solving the Traveling Salesman Problem," 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), 2021, pp. 652-656, doi: 10.1109/ElConRus51938.2021.9396448.
[5] A. Benzoni, C. Yugma, P. Bect and A. Planchais, "Allocating Reticles in an Automated Stocker for Semiconductor Manufacturing Facility," 2020 Winter Simulation Conference (WSC), 2020, pp. 1711-1717, doi: 10.1109/WSC48552.2020.9383933.
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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.