Multi-objective Fab Scheduling: Exploring Scenarios and Tradeoffs for Better Decision Making
Building and maintaining any form of scheduling solution to be flexible yet robust is not an easy undertaking. Commonly, fab managers have resorted to rule-based dispatch systems or other discrete-event simulation software to estimate how their fab will play out in the near future. Often this requires deciding a specific KPI that is important to the fab up-front; do I care more about getting wafers out the door, or reducing the cycle time of those wafers?
Competing objectives challenge
As a fab manager, there are a number of competing objectives to balance on the shop floor that all impact the profitability of the fab. Whether that be reliably delivering to customers their contractual quantities on time, or ensuring that fab research and development iteration time is kept low, fabs need a flexible, configurable scheduling solution that can produce a variety of schedules which account for these tradeoffs. At Flexciton, we call this “multi-objective” scheduling; optimizing the factory plan whilst considering several independent KPIs that, in this case, are fundamentally at odds with one another. This article explores Flexciton’s approach to multi-objective scheduling and how we expose simple configurations to the fab manager, whilst allowing our scheduling engine to ultimately decide on how that configuration plays out in the fab.
If there is no automated real-time dispatch system in the fab, determining the "best" schedule is a very complex procedure that cannot even be accomplished with advanced spreadsheet models. Assuming that the fab is advanced enough such that a dispatch system is in place, it will likely only consider "local" decisions pertaining to the lots that are immediately available to the dispatch system at the time the decision is made.
Dispatch systems typically do not have the configurability to adjust the user's incremental utility with respect to throughput and cycle time; they typically adhere to a series or hierarchy of rules that are tuned to consider exactly one KPI. Therefore to change the objective of the dispatch system would require rewriting these rules; an often time-consuming exercise that requires advanced technical knowledge of the dispatch system. This makes it almost impossible or otherwise very time consuming to trial various configurations of the fab manager’s preferences.
Balancing various objectives for best results
The Flexciton optimization engine is a multi-objective solution that can linearly balance various KPIs according to user-chosen weights. As these weights are exposed to the end-user, this renders the possibility of running many different scenarios with varying preferences trivial. Fab managers can have access to the specific weight values themselves or work with our expert optimization engineers to select from a handful of high-level configurations and the solution will select appropriate weights itself.
To properly understand the flexibility of the engine, we will now step through four case studies. The goal is to compare how, given the same dataset, slightly different objective configurations impact the solution that is returned by accounting for the change in preferences.
We present a schedule of nine tools from across five toolsets with seventy lots of a mix of 65% Priority1 lots. Each lot can go to a random subset of tools within a single toolset.
The schedule will then be tested against four runs:
- Produced by a dispatch system with heuristic rules
- Optimized for cycle time
- Optimized for the on-time delivery of wafers
- Balanced optimization considering both cycle time and OTD
For each of these scenarios, we will present two gantt charts; one labelled with the “Queueing Time” of each lot (aka “rack time”) and another labelled with the “Late Time” of each lot. Late time refers to the duration by which the lot completed processing after its due date. If it was not late, the label reads “0s” since we do not consider being more early as being more favourable. Lots that are considered high priority (Priority 1 to 3) are given a circle badge indicating such. Low priority lots are Priority 4 through 10. Each lot is coloured according to this priority class.
Case study #1: base case - greedy dispatch
To begin, we’ll present how a schedule could look when produced by a dispatch heuristic that does not consider the future arrivals of wafers, but simply what is currently available in front of a tool. The greedy rule here is to just dispatch the highest priority wafer on the rack at the point the tool is idle.
In the above example, the high-priority wafers have to wait due to the system only considering what’s on the rack and therefore dispatching the low-priority wafers that are ready to go.
It should be noted that such a strategy is great for improving overall throughput and cycle time since the machine idle time is reduced by constantly dispatching wafers. This has the side effect of delivering all-bar-one of the wafers on time. In reality though, not all lots are equal and fab managers care a great deal more about certain high-priority lots thus making the scheduling problem quite a bit trickier.
Unfortunately, in order to reconfigure the system to place greater importance upon the high-priority wafers and dispatch them first would require complex rewriting of the dispatch rules to “look ahead” at the wafers that are not yet on the rack, and are arriving shortly. The dispatcher would then elect to keep the machine idle in order to reduce the high-priority wafer cycle time.
Case study #2: Optimize for high-priority-lot cycle time
Instead of modifying the RTD rules, we can emulate what that would look like by running our optimization engine whilst optimizing for the cycle time of high-priority lots:
The low priority lots at the front of the schedule are replaced with high-priority lots so that they can be dispatched as soon as they arrive. These low priority lots have been pushed to the back of the schedule with non-zero rack time (since the cycle time of high priority lots matters so much more). Naturally this is at the cost of overall average cycle time which has suffered by 23% in order to improve Priority1 cycle time by 11%. Also note that on tool “SBXF/115”, our scheduling solution has pushed the Priority2 (orange) and the Priority10 (green) lots later so that the Priority1 (red) lots are rushed through with zero rack time.
Case study #3: Optimize for on-time delivery
With optimisation, there are no additional changes required to increase the flexibility of the system. We simply describe what a good schedule looks like using the multi-objective function and the optimizer does the rest. Subtle tweaks to this function will inevitably produce very different schedules. Now let’s take a look at how the schedule alters when we want to maximise solely on-time delivery.
As expected, cycle time is quite a bit worse than previously however now there are no lots delivered late. This is very similar to the original schedule produced by simple dispatch rules. The low-priority lots have been brought forward so that they are delivered on time and the cycle time of the high-priority lots suffer as a result.
Case study #4: Optimize for both
Finally, the main purpose of this article is to illustrate the ease of considering both KPIs with some relative weight simultaneously.
Note that the KPIs of cycle time and throughput are slightly worse than when that was the sole KPI being optimised. The key is that both are better than when the other KPI was being optimized. This balance is entirely in the hands of the fab manager. We maintain roughly the same cycle time of high-priority lots as when optimising for cycle time and fewer lots are late than when optimizing only cycle time.
Summary and Conclusions
This article has provided a number of ways that illustrate how optimization can be considered both more flexible and robust than heuristics that cannot effectively search the global solution space.
The engine is simple to tune due to the exposed weights and/or configurations presented to the fab manager which allow a high degree of customisation both with respect to the objective function and wafer priorities. This flexibility allows us to easily consider complex hierarchical objectives found in semiconductor manufacturing such as “optimise high-priority cycle time as long as no P1-8 lots are late” or “optimise batching efficiency (perhaps due to operator constraints) and then high-priority cycle time”. Ultimately, our solution is a market-leading scheduler that will realise true KPI improvements on your live wafer fabrication data.
Flexciton is currently offering the Fab Scheduling Audit free of charge. To enquire, please click here.
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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.