Scheduling as a Cornerstone of the Smart Factory [Part 2]
In Part 1 of this blog, we focused on use cases where lots are scheduled on tools and how advanced scheduling gives users the ability to optimize for future decisions as well as real-time. When we say "advanced," we are referring to autonomous, optimization-based solutions. Our emphasis was primarily on how scheduling can enhance productivity in a fab today. In Part 2, however, we’ll delve further into its potential for fabs in the not too distant future.
Previously, I discussed how task lists are typically associated with human workers. However, it is worth noting that task lists can also be applied to automated systems such as automated guided vehicles (AGVs) and automated material handling systems (AMHS) with the use of an advanced scheduler. With task lists, an advanced scheduler can not only determine which lot is assigned to which tool and when, but also which operator – or robot – will be serving the tool. There’s a whole set of new opportunities that arise with that, as humans and robots, just like tools, have a limited capacity that can be optimally utilised. It’s clear then that the possibilities for advanced scheduling go beyond the stand-alone Industry 4.0 applications and have the potential to integrate vast amounts of fab data into a holistic system.
One of the use cases of such a holistic system is described later on in this blog as a type of ‘digital twin’, but the capabilities of an advanced scheduling system go beyond that. With a digital twin concept, the human is still very much inside the cockpit. An advanced scheduling system, on the other hand, is more like an autopilot, augmenting the capabilities of other systems and taking control of manufacturing decisions when necessary. As such, advanced scheduling is a cornerstone of the so-called ‘smart factory’. Let’s try to understand the huge array of benefits it can bring. First, we’ll cover a couple of use cases that can benefit the manufacturers. Second, we’ll share some thoughts on how advanced scheduling aligns with the idea behind Industry 5.0 and how the technology can serve ourselves as humans.
- WIP Transport Scheduling
Once a lot is intelligently scheduled, we know when to process it and on which tool. The lot can be transported to that tool’s specific staging rack just before it gets processed. It enables fabs to eliminate waste by optimizing transport capacity, which removes the likelihood of a lot being transported at half capacity only for it to wait in queue. Transport scheduling also enables splitting logistics and processing workflows; some workers focus on keeping the tools running, others focus on getting the lots to the tools in time. Multi-cleanroom fabs will make better use of their capacity in areas that for logistical reasons are not preferred. Which means no more remote idle machines waiting for a lot that doesn’t arrive.
- Dynamic Capacity Models
With better control of lot processing, intra-fab logistics, and workforce planning, we get a more realistic view on the true capacity of a factory. We call it a dynamic capacity model, resembling the idea of a digital twin of a production plant. A dynamic capacity model better reflects the current state, loading and dynamics in a factory, as opposed to the static capacity models commonly used. Until now in wafer fabs, dynamic capacity models have at best been approximated by what-if scenarios in simulation models, but the potential goes beyond that. When playing around with different scenarios – e.g. when to plan maintenance or shutdowns, which availability increase has the most impact on the whole factory, what’s the effect of frequent product mix changes, what lead times to expect and so on – it should allow factories to better judge the impact of their decisions. Optimization can even help by not only interpreting the outcome, but suggesting the best decision for a fab’s goals.
- Multi-factory Models & Supply Chain Planning
Eventually, dynamic capacity models could scale to corporate level in multi-factory models. Further up, these models could feed into supply chain planning software. During the supply chain crisis, it was striking to see how disconnected sales and operations planning cycles in semiconductors were from the actual operational challenges of factories. Part of it was because of models that don’t properly comprehend the actual situation the factory was in. Fabs were treated as black boxes with a simple input and output signal, but just because you have promised your customers a sooner delivery date, it doesn’t mean it will happen automatically. You need a driver towards that new target, and that’s where advanced scheduling software helps, by optimizing towards shorter lead times. Its integration into dynamic capacity models and supply chain planning software would lead to more reliable input for inventory and order fulfilment optimization engines. This translates into lower inventory costs and better delivery performance of a company.
Eventually, we want technology to help us overcome the challenges we face as humans. From what has been written so far, this blog might give the impression that this technology is primarily serving profitability. But becoming a smart factory doesn’t necessarily contradict with a human-centric approach. Industry 5.0 is the theoretical concept that’s been introduced for that. It counters the illusion that the future of manufacturing is one in which humans play a minor role. Instead, we should embrace both the capabilities of new technologies, as well as those of humans and find synergies to make the best of both worlds. While Industry 4.0 can do a great job in automating repetitive tasks or making sense out of masses of data, humans have the advantage of better interpretation of context, require fewer data points to understand, and can make value trade-offs. Humans will not miraculously disappear from the factory shop floor, so we’ll benefit from thinking about how these advanced technologies can harmoniously coexist with people and yield mutually beneficial outcomes.
- Staff Workload Balancing
The obvious fear with advanced scheduling is that operators and technicians will turn into de facto robots, where only adherence is of importance when aiming to get more out of the workforce. Let’s turn that thought up-side-down: what if the same work could be better distributed amongst the team by offloading peaks to underloaded co-workers? Advanced scheduling can better predict and hence properly distribute work aligned with an individual's availability and level of training. Also the workflow itself - the number and order of actions to perform - can be streamlined to lower physical and mental workload.
- Training, Evaluation & Individual Productivity
With detailed production schedules, any lack of staff or training becomes directly visible and quantifiable. Hiring and training programs could become more timely and data-driven, just as annual evaluations will become less subject to biases of the manager. Even on-the-spot productivity can be monitored and optimised. This may sound like a “Big Brother” concept, but compare it with the advancement of sports analytics and medicine in the last decade. Professional athletes don’t complain about data integrity and privacy issues, because (1) it’s part of their job and (2) it helps them in what they want to achieve. If athletes ignore their data, they simply don’t reach the top anymore. Similarly, the fourth and fifth industrial revolution will bring staffing to higher levels of productivity, not because they are squeezed out more, but because the data will reveal where there’s room for improvement or when a red line is about to be crossed.
Given the increasing scale and complexity described above, significant computational power and data storage capabilities will be necessary. This makes it likely that cloud-based technology will be adopted to facilitate the transition to smart factories. Although many fabs are currently far from achieving smart factory status, it is clear that the industry is moving in this direction. Therefore, factory managers must acknowledge that the transition to becoming a smart factory is not just a concern for the future and must be implemented within a realistic timeframe. The foundations for this transition, including employee readiness, are already being established today. And given the use cases discussed, let there be no doubt that advanced scheduling will play an integral part in the next generation of wafer fabs.
Author: Ben Van Damme, Industrial Engineer and Business Consultant
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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.