A Hot Topic: What Makes Scheduling the Diffusion Area so Challenging? [Tech Paper Review]
The diffusion area is particularly important to the smooth operation of a wafer fab. Not only does it receive raw wafers at the very beginning of the fabrication process but it also interacts with many other areas of the fab.
The challenge in scheduling diffusion area lies in the particularities involved in its operation:
- Re-entrant flows: Furnaces are loaded with wafers that may have already been processed by other furnaces or wet benches.
- Batching machine: Several lots of 1-25 wafers each can be processed together in one batch if they run the same recipe.
- Time constraints: The diffusion area has timelinks that determine the maximum time a wafer has to move to a subsequent tool to avoid rework or scrappage.
- Dummy wafers: Used in furnaces, for example, to fill out a lot when a full lot is required or to protect the most exposed ends of a lot to ensure uniformity.
Balancing very long fixed processing times on batching tools with the features mentioned above makes it exceptionally tricky to get solid production KPIs on diffusion furnaces. Currently, fabs often resort to using simplistic "minimum batch size" dispatch rules that try to balance building full batches (to maximise the utilisation of the tool) with queue time and the risk of violating a timelink constraint.
As a result of these characteristics, it's very common for diffusion areas to become a bottleneck if not managed correctly – negatively impacting the production KPIs of the rest of the fab.
This is what prompts the exploration of more novel scheduling methods, such as the one we'll be discussing in this article.
Case Study: Job Scheduling of Diffusion Furnaces
To explore the various ways to schedule diffusion areas, we review the paper “Job scheduling of diffusion furnaces in semiconductor fabrication facilities” by Wu et al. (2021) that describes a new scheduling system that was deployed live in a 200mm GlobalFoundries wafer fab.
Fab Characteristics and the Need for Change
The fab that the system was implemented into consisted of the following attributes; approximately 300 products, 500 recipes, and 4500 lots daily at the diffusion area which is host to more than 90 furnaces.
The approach was designed to build schedules aiming to maximise the weighted number of moves. The weights were based on the product of the wafer and the stage of production for which moves were being calculated.
Schedules were planned by 6 operators several times a day, taking up to 6 hours a day per operator on average. The quality of schedules are also impacted by the judgement and experience of operators which led to suboptimal decisions and lower efficiency.
The Approach to Scheduling
The heuristic model used in the system took about nine months to be built, whilst the system implementation took one year and a half, with the majority of time being spent on clarifying user requirements and collecting data.
The problem was addressed with techniques called Dynamic Programming and Genetic Algorithm:
Dynamic Programming consists of breaking down a large problem that contains many possible solutions into several sequential sub-problems that are easier to solve. Each of these subproblems is solved one at a time, such that each solution feeds into the next problem.
Each one of these sub-problems is then solved using a modified version of the Genetic Algorithm, a meta-heuristic procedure commonly used for large optimization problems.
Results
After implementing live in the fab, average daily weighted moves per tool improved by 4.1% in the first two months of trials when compared to the 2 months before deployment. When tested offline and compared to historical data, the approach increased the number of moves by 23.4% and the average batch size by 4.1% while reducing tool idling by 62.8%. The authors argue the fab was short of staff, subject to varying demand and product mix over time, and with operators still not fully adhering to the new schedules.
It is also expected that, by exploring the full potential of the system, cycle time can be reduced by 1.8 days and that an increase of eleven thousand moves can be achieved, leading to an estimated financial saving of $2M USD per year.
Flexciton’s View
A lot of the academic literature on scheduling furnaces tend to omit some rather critical details such as missing constraints, only being tested on small test datasets, or they are prohibitively slow in live environments.
The reviewed approach stands out by addressing these issues and successfully implementing a complex scheduling system in a fab that brings measurable improvements to the number of moves, batch size and tool idleness. The model accounts for many relevant details such as preventive maintenance, lots with tool dedications at certain steps and different lot priorities.
Nevertheless, as specialists in scheduling, we have spotted weaknesses in the approach where we believe there are opportunities to make it even more robust and versatile, whilst delivering even better results:
1. Schedule updates every 40 minutes: unexpected events (e.g., machine downtime) can take longer than schedule creation time. Suppose a furnace goes offline 10 minutes after the start of the generation of a new schedule. Two things will happen:
a. Schedule being built (unaware of the machine outage) may dispatch lots to the offline tool.
b. Machine outage will be handled only in the next schedule, 70 minutes after the machine went down.
2. Diffusion furnaces scheduled in isolation: Optimizing diffusion furnaces in isolation may cause other machines and areas to be neglected – resulting in suboptimal decisions. For example, since these clean tools feed other parts of the fab, there’s no guarantee that the necessary WIP will arrive at the furnaces to accommodate the optimized schedule having not taken clean capacity into account.
3. Assumption that transportation time of wafers is negligible compared to the processing time: despite the long processing times in furnaces, it’d be interesting to test transportation times in the model to confirm if it’s indeed irrelevant for scheduling or if it brings different decisions to the final schedule.
4. Loading and unloading time not addressed in the approach: Unlike processing times that are fixed, the loading and unloading times can still vary with the number of wafers.
Flexciton’s Way
Flexciton’s solution has been built to schedule any area of a fab through multi-objective optimization, handling multiple fab KPIs with their trade-offs and sending an optimized schedule to the fab every 5 minutes. Below, we outline the main features of how we tackle the main challenges of furnaces scheduling:
1. A fab-wide approach: our optimization engine schedules furnaces not in isolation but together with other machines across the fab. We utilise a holistic approach, looking ahead for bottlenecks across the entire factory and account for the existence of bottleneck tools when making scheduling decisions. For instance, a lower priority wafer may be dispatched before a high priority one if the former is going to a low-utilisation machine while the latter is going to a bottleneck in its next step.
2. Criticality of time constraints: whilst eliminating violations of timelinks, we account for the different criticalities they may present, be it because of the machines and recipes used or due to wafer priorities. This means that under a situation where one of two timelinks must be violated for reasons beyond our control, the less critical timelink will be violated.
3. Multi-objective optimization: We balance multiple KPIs simultaneously and handle their trade-offs through user-defined weights. For example, objectives such as “minimise timelink violations” and “minimise cycle time” can receive different weights depending on the desired behaviour in the fab. This directly impacts decisions such as “how long should a high priority wafer wait for a full batch?”.
4. New schedules every 5 minutes: Our technology is based on a hybrid approach that combines Mixed Integer Linear Programming (MILP) with heuristic and decomposition techniques, enabling the delivery of high-quality schedules to the fab every 5 minutes.
5. Change management: Adherence by operators and managers to a new scheduling system and its decisions is among the main post-implementation challenges. Because of that, our deployments follow a rigorous plan that helps foster a higher adoption of the technology. We also use detailed Gantt charts to aid the visualisation of schedules, which facilitates a solid understanding of decisions made which in turn enables higher adherence from operators.
As explored in this article, scheduling diffusion furnaces can be an extremely complex task. This is true even from a computational standpoint, leading many semiconductor fabs to rely on the judgement and experience of their operators at the cost of obtaining suboptimal and inconsistent schedules that take hours to generate. On the other hand, the usage of some fast-scheduling systems may mean leaving some constraints behind, ignoring different KPIs or not observing the fab in its entirety.
At Flexciton, we combine the best of both worlds and bring fast optimal decisions while fostering technology adoption at all hierarchies of the fab.
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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.
From guaranteed KPI improvements to reducing fab workload by 50%, this blog introduces some of the benefits of Autonomous Scheduling Technology (AST) and how it contrasts with the scheduling status quo.
In the fast-paced world of semiconductor manufacturing, efficient production scheduling is crucial for chipmakers to maintain competitiveness and profitability. The scheduling methods used in wafer fabs can be classified into two main categories: heuristics and mathematical optimization. Both methods aim to achieve the same goal: to provide the best schedules within their capabilities. However, because they utilize different problem-solving methodologies, the outcome is dramatically different. Simply put, heuristics generates solutions by making decisions based on if-then rules predefined by a human, while optimization algorithms search through billions of possible scenarios to automatically select the most optimal one.
Autonomous Scheduling Technology (AST) features mathematical optimization combined with smart decomposition, allowing the quick delivery of optimal production schedules. Whether you are a fab manager or industrial engineer, the experience and results of applying Autonomous Scheduling in your fab are fundamentally different compared to a heuristic scheduler.
Here's how switching to AST can impact your fab.
Consistent and Superior KPIs Guaranteed
Autonomous Scheduling Technology (AST) evaluates all constraints and variables in the production process simultaneously, ensuring optimal decision-making. Unlike heuristics schedulers, which require ongoing trial and error with if-then rules to solve the problem, AST allows the user to balance trade-offs between high level fab objectives. With its forward-looking capability, it can assess the consequences of scheduling decisions across the entire production horizon and generate schedules that guarantee that the fab's global objectives are met. The tests we have conducted against a heuristic-based scheduler have proven that Autonomous Scheduling delivered superior results. Book a demo to find out more.
Never miss a shipment
One of the most critical aspects of fab operations is meeting On-Time-Delivery deadlines. With AST, schedules are optimized towards specific fab objectives, ensuring that production targets align with business goals. Mark Patton, Director of Manufacturing Seagate Springtown, confirmed that adopting Autonomous Scheduling in his fab allowed him to:
"improve our predictability of delivery by meeting weekly customer commits. With a lengthy cycle time build, this predictability and linearity has been key to enabling the successful delivery and execution of meeting commits consistently."
Reduced workload (by at least 50%)
The reactive nature of heuristic-based schedulers places a significant burden on industrial engineers, who must constantly – and manually – tune rules and adjust parameters. To ensure these systems run optimally, fab managers must dedicate at least one industrial engineer to working full-time on maintaining them. With AST, the workload is significantly reduced due to the system's ability to optimize schedules autonomously (without human intervention). This means there will be no more firefighting when the WIP profile changes. This reduction in labor intensity frees up engineers to engage in value-added activities.
Reduced rework, improved yield
Some areas of a fab are notoriously challenging to optimize. For example, the diffusion and clean area is home to very complex time constraints, also known as timelinks. When timelinks are violated, wafers either require rework or must be scrapped. Either way, it's a considerable cost for a fab. Autonomous Scheduling Technology is highly effective at managing conflicting KPIs with its multi-objective optimization capabilities. AST dynamically adjusts to changes in the fabrication process to consistently eliminate timelink violations whilst maximizing throughput.
Confidence in Balancing Trade-offs
With its ability to look ahead, Autonomous Scheduling Technology can predict the consequences of different trade-off settings. This capability is particularly valuable when balancing competing objectives like throughput and cycle time. Users of legacy schedulers would typically move sliders to adjust the settings and wait a considerable amount of time to assess whether the adjustments generate the desired scheduling behavior. If not, further iterations are required, and the process repeats. In contrast, AST can evaluate billions of potential scenarios and determine the optimal balance between conflicting goals. For example, it can predict the exact impact of prioritizing larger batches over shorter cycle times, allowing fab managers to make informed decisions with confidence. This strategic foresight ensures that the best possible trade-offs are made, optimizing the whole fab to meet overarching objectives.
Conclusion
In an industry where efficiency and precision are paramount, Autonomous Scheduling Technology provides a distinct competitive advantage. It equips fabs with the tools to consistently outperform legacy systems, streamline operations, and ultimately drive greater profitability. By investing today in upgrading their legacy scheduling systems to Autonomous Scheduling Technology, wafer fabs are not only optimizing their current operations but also taking an important step toward the autonomous fab of the future.
Now available to download: our new Autonomous Scheduling Technology White Paper
We have just released a new White Paper on Autonomous Scheduling Technology (AST) with insights into the latest advancements and benefits.
Click here to read it.