C is for Cycle Time [Part 1]
This two-part article aims to explain how we can improve cycle time in front-end semiconductor manufacturing through innovative solutions, moving beyond conventional lean manufacturing approaches. In part 1, we will discuss the importance of cycle time for semiconductor manufacturers and introduce the operating curve to relate cycle time to factory utilization. Part 2 will then explore strategies to enhance cycle time through advanced scheduling solutions, contrasting them with traditional methods.
Part 1
Why manufacturers care about cycle time
Cycle time, the time to complete and ship products, is crucial for manufacturers. James P. Ignazio, in Optimizing Factory Performance, noted that top-tier manufacturers like Ford and Toyota have historically pursued the same goal to outpace competitors: speed [1]. This speed is achieved through fast factory cycle times.
This emphasis on speed had tangible benefits: Ford, for instance, could afford to pay workers double the average wage while dominating the automotive market. The Arsenal of Venice's accelerated ship assembly secured its status as a dominant city-state. Similarly, fast factory cycle times were central to Toyota’s successful lean manufacturing approach.
Furthermore, semiconductor manufacturers grapple with extended cycle times that can often span 24 weeks [2]. This article will focus on manufacturing processes in front-end wafer fabs as their contribution to the end product, such as a chip or hard drive disk head, spans several months. In contrast, back-end processes can be completed in a matter of weeks [3]. However, the principles discussed apply universally to back-end fabs without sacrificing generality.
Why Short Cycle Times Matter for Front-end Wafer Fabs
- Revenue acceleration: The quicker products reach customers, the faster revenue streams in. However, quantifying the precise financial impact due to cycle time is intricate and beyond this article's scope.
- Competitive advantage: Gaining a competitive advantage involves reducing cycle time in R&D wafers, which accelerates product launches. More than 20% of front-end fab production lines can be used for R&D wafer testing and iteration. Swift deliveries enhance a company's reputation, leading to more contracts. At the 2022 Winter Simulation Conference, Micron highlighted their rapid advancements: maturing 30% quicker in DRAM (five months ahead of the previous node) and 20% in NAND (a year faster than the prior node). See Figure 1.
- Agility in market responsiveness: A fab with shorter cycle times can swiftly adjust to market fluctuations, whether that is a surge in demand or a shift in product preferences, such as changes in product mix. It can also respond faster to changes in customer requirements.
- Risk mitigation: The shorter the cycle time, the quicker a fab can respond once defects have been detected as it takes less time to perform rework.
- Inventory management: Lower cycle times can reduce the amount of work-in-progress (WIP) in buffers or racks (intermediate stock), or stock at the end of the production line. This not only liberates tied-up capital but also wafers can move quicker with less WIP in the fab as it is shown in a later section introducing the operating curve.
Achieving Predictable Cycle Time
Less variability in cycle time helps a wafer fab to achieve better predictability in the manufacturing process. Predictability enables optimal resource allocation; for instance, operators can be positioned at fab toolsets (known as workstations) based on anticipated workload from cycle time predictions. Recognizing idle periods of tools allows for improved maintenance scheduling which will result in reduction in unplanned maintenance. In an upcoming article (Part 2), we'll explore how synchronizing maintenance with production can further shorten cycle times.
Measuring and monitoring the cycle time improves overall fab performance
Measuring and monitoring cycle times aids in identifying deviations from an expected variability. This, in turn, promptly highlights underlying operational issues, facilitating quicker issue resolution. Additionally, it assists industrial engineers in pinpointing bottlenecks, enabling a focused analysis of root causes and prompt corrective actions.
Supply chain stakeholders usually fail to understand the impact of cycle time
In the semiconductor industry, cycle time plays a pivotal role in broader supply chain orchestration:
- A predictable cycle time informs suppliers when to provide fresh batches of raw materials.
- Furthermore, it influences the downstream processes of Assembly & Test Operations (back-end facilities). Back-end facilities with a cycle time of less than a week gain enhanced predictability, allowing for more effective allocation of capacity and resources.
- Predictable cycle times will also inform safety inventory levels, freeing capital and optimizing storage space.
Cycle time is a component of the total lead time of a product (it also includes procurement, transportation, etc). Therefore, total lead time can be reduced if the long cycle times in the front-end wafer fabs are reduced. A reliable cycle time nurtures trust with suppliers, laying the foundation for favorable partnerships and agreements. In essence, cycle time is not just about production; it's the heartbeat of the semiconductor supply chain ecosystem.
Understanding how cycle time impacts product delivery times is essential for the semiconductor industry. In some analyses, you could see that cycle time is confused with capacity, as the authors in a McKinsey article stated “Even with fabs operating at full capacity, they have not been able to meet demand, resulting in product lead times of six months or longer” [4]. On the contrary, in a fab operating at full capacity, lead times of the products will increase as the average cycle time of manufacturing is increasing.
How to measure cycle time
Fab Cycle Time
The fab cycle time metric defines the time required to produce a finished product in a wafer fab. The general cycle time term is also used to measure the time required to complete a specific process step (e.g. etching, coating) in a toolset, known as process step cycle time. The fab cycle time consists of the following time components as can be seen in Figure 2:
- Value-added processing time, which is the time taken to transform or assemble the unfinished product, which is a wafer in our case.
- Non-value-added processing time includes the time taken for inspection and testing, as well as the time for transferring the wafer between different steps.
- Time to prepare the products for processing: this refers to the time operators or tools required to form a batch, i.e. to select which lots should be bundled together for processing.
- Queue time: the time spent where the unfinished wafer is waiting to be processed because the tool required is busy, due to the tool processing another batch or undergoing maintenance.
To measure and monitor cycle time, wafer fabs must track transactional data for each lot, capturing timestamps for events like the beginning and completion of processing at a tool. This data is gathered and stored by a Manufacturing Execution System (MES). Such transactional information can be utilized for historical operations analysis or for constructing models to forecast cycle times influenced by different operational factors. This foundation is crucial for formulating the operational curve of the fab, which we'll delve into in the subsequent part of this blog. As outlined in an article by Deenen et al., there are methods to develop data-driven simulations that accurately predict future cycle times [3].
Fab operating curve: Fab Cycle Time versus Factory Utilization
As we mentioned earlier, historic data can be used to generate the operating curve of a fab which describes the cycle time in relation to the factory utilization. Figure 3 shows the graph of the fab cycle time in days versus the utilization of the fab (%). The utilization of the fab is defined as the WIP divided by the total capacity of the fab.
We have found this method useful in understanding the fundamental principles of cycle time. The operating curve helps to explain how factory physics impact fab KPIs such as cycle time and fab utilization by showing the changes in the operating points:
- The horizontal line, representing the summation of raw process times (known as theoretical cycle time), envisions a scenario with zero queuing time in the fab. This illustrates the impact of queuing time on cycle time as we increase WIP, moving right on the x-axis. Accumulation of queuing time becomes inevitable with the introduction of more WIP in the fab.
- The ideal operating curve represents the operation of a wafer-fab assuming that there is zero waste. This curve defines the minimum achievable cycle time for each fab load and the difference between this curve and theoretical cycle time is because of real life variability in the fab that cannot be eliminated completely, e.g. unplanned maintenances, inconsistent tool processing times.
- The cycle time tends to go to infinity, when you move towards 100% utilization of the fab.
- The actual operating curve, cycle time versus factory utilization, represents the current fab’s operation considering all the inefficiencies such as excessive inventory, variability in operations, idle times, poor batching and rework.
- Both curves assume average or constant values of the operational parameters of the fab for example a fixed number of tools installed, an average availability of each tool and labor, and a constant product mix.
- The actual operating curve describes the impact on cycle time if we load the fab with more WIP as shown in Figure 4. The fab management could use this information to make a decision about the trade-off between cycle time and factory utilization. Higher fab utilization is associated with a higher throughput (i.e. number of wafers per unit of time).
In Figure 3, you can see that the current fab cycle time is 40 days when the factory utilization is at 60%. Theoretically, we could reduce the cycle time to 22 days. The difference between these two points is due to the inefficiencies that contribute to the factory cycle time as explained in the introduction of this section. In Part 2 of this blog, we will explore the various types of inefficiencies and examine how innovation can shift the operating curve to achieve lower cycle times while maintaining the same fab utilization.
Summary
In summary, cycle time is not merely a production metric but the very pulse of the semiconductor manufacturing and supply chain. It governs revenues, shapes market responsiveness, and is pivotal in driving innovation. By understanding its nuances, semiconductor companies can not only optimize their operations but also gain a competitive edge. And while we've scratched the surface on its significance, the question remains: how can we further reduce and refine it? In part 2 of the C for Cycle Time blog, we will discover innovative techniques that promise to revolutionize cycle time management in wafer fabs.
Author: Dennis Xenos, CTO and Cofounder, Flexciton
References
- [1] James P. Ignizio, 2009 ,Optimizing Factory Performance: Cost-Effective Ways to Achieve Significant and Sustainable Improvement 1st Edition, McGraw-Hill, ISBN 978-0-07-163285-0
- [2] Semiconductor Industry Association, 2021, Blog, URL
- [3] Deenen, P.C., Middelhuis, J., Akcay, A. et al., 2023, Data-driven aggregate modeling of a semiconductor wafer fab to predict WIP levels and cycle time distributions. Flex Serv Manuf J. https://doi.org/10.1007/s10696-023-09501-1
- [4] Ondrej Burkacky, Marc de Jong, and Julia Dragon, 2022, Strategies to lead in the semiconductor world, McKinsey Article, URL.
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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.