Is Fear Holding Back The Chip Industry’s Future In The Cloud?
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The semiconductor industry is at the cutting edge of technology – so why is it still so nervous about the cloud? Persisting with an outmoded security model means missing out on significant gains in manufacturing.
Only the paranoid survive?
Perhaps more than any other sector in the world, the semiconductor industry is incredibly protective of its intellectual property (IP). Given the centrality of the silicon chip to modern life, that’s not surprising – companies are in a constant arms race to design and develop ever more sophisticated chips to meet the never-ending demand for innovation from their customers. A design breakthrough could be worth billions of dollars, and so the security of the relevant data is paramount.
And that’s not the only threat that keeps semi co security teams awake at night – there’s the security of the actual chips themselves to consider. An ongoing fear within both the industry and among government security agencies is that rogue code may be inserted into a chip either during development or the manufacturing process, making any system it becomes part of vulnerable to attack.
In fact, security of manufacturing – with many companies now sub-contracting to facilities in Asia – has been explicitly cited as a key reason for building more fabs in the US. In March 2022, President Joe Biden said that semiconductors are “so critical to our national security… that we’re going to create rules to allow us to pay a little more for them if they’re made in America.” In other words, security fears are so intense that the industry is willing to put prices up just for the supposed reassurance of having chips that aren’t produced overseas.
Although Biden’s worries over the threats to national security are not cloud related, they feed into a culture of fear that has become embedded into the semiconductor industry, hindering its advancement towards next-gen technologies.
The cloud revolution
The cloud has revolutionised the way that business works in the 21st century in a number of ways. For a start, it’s decentralised the IT function – applications that would previously have resided in on-premise server rooms are now accessed as a service via the cloud. This has significantly simplified the set-up and running of satellite offices and local branches because there’s no need to house and manage IT hardware at every location – all that’s needed is a connection to the internet.
But for hi-tech companies, the real advantage of the cloud is the ability to access vast amounts of computing power on demand. Whether it’s for data crunching a massive set of figures, running an AI model through its paces, or simply trying to crack a really complex problem, the muscle provided by cloud computing can dramatically speed the process up.
On the face of it, this would make the semiconductor industry an obvious candidate for the widespread adoption of cloud technology. But that hasn’t been the case. Limited adoption has taken place – though usually relating to ‘non-critical’ business functions – but compared to the companies they serve, semi cos have been conspicuously slow to embrace the potential of the cloud.
Outmoded assumptions and intransigence
For an industry on the cutting edge of technological innovation, the reasoning behind this state of affairs seems to be based on outdated assumptions, an indication perhaps of just how embedded the fear culture is. The security philosophy at many chip makers is still predicated on each separate facility being a castle under siege that needs to be protected from external attack. The idea of willingly opening up these defences to the cloud is anathema.
Another factor holding back the full embrace of the cloud at chip companies and fabs is the fear of change. Many IT and security managers simply don’t recognise the new world of serverless functionality that the cloud can bring, and are quite happy to stick with the existing model. And there are IT teams that do understand the possibilities of cloud, but are frightened by what they imagine will be a massive upheaval of their working lives and environment, from having to create new security policies to potentially making themselves redundant. Without the pressure to change that has come from the top in other industries, IT itself is blocking cloud adoption.
Yet as both design and manufacturing processes become more complex, this reluctance to change isn’t tenable in the long-term. As chips become more and more sophisticated, the need to access computing power at scale will increase – and that means companies either building bigger server farms and private data centres, or properly embracing the cloud paradigm.
The fact is that cloud security has improved immeasurably over the past decade. According to a recent report from Accenture, “Today’s cloud solutions offer enhanced security and automation technologies that aren’t available for on-premise systems, making cloud a better option for preventing IP theft.” And refusing to move with the times because it threatens to disrupt the status quo is an increasingly questionable excuse from an industry built on pushing the technological envelope.
Ultimately, semiconductor companies have only fear and intransigence holding them back from total cloud adoption.
The end of on-premise production scheduling?
If the industry is to continue to innovate and keep up with the demands of its customers, it needs to produce highly sophisticated, next generation chips at scale. The only way to do that is by adopting smart manufacturing practices and technologies - and that means fully embracing the cloud. Why? Because current on-premises scheduling systems are no longer fit for purpose to handle the new levels of manufacturing complexity that next gen chips demand.
In an enclosed, siloed environment, such as exists in most current fabs, a typical on-premise scheduling system will only have access to so much computing power. Traditionally, these constraints have resulted in a reliance on heuristics to predict and control production workflow, as this is the best that can be achieved with the resources available. However, although these systems often use real-time data, the decisions they make are still based on rules that are created based on human experience from the past. The dynamic nature of a fab means that these rules are never going to stay pertinent, thus resulting in suboptimal production decisions.
By connecting the fab to the cloud, these power constraints disappear – and with them the restrictions that previously forced fabs to use heuristics-based scheduling. With access to a new magnitude of compute, companies can deploy more sophisticated systems able to schedule production based on real-time information, and thus optimize the manufacturing process.
Thanks to the power of the cloud, this next generation of scheduling systems is able to use complex mathematical algorithms to search through the billions of possible WIP permutations and make the best scheduling decision with present-time accuracy. This AI-based approach to scheduling requires a huge amount of computing power to rapidly work out the fab’s optimal position, but the cloud makes it possible to perform these calculations at unparalleled speed.
In theory, it is possible to get good computational power on-premise. The system would most likely be chosen based on what is cost-effective at the time and the power needed to solve the problem a fab had on that day. However, new computational power becomes more available and cost effective all the time. Moreover, fab complexity can easily change. For example, introducing a larger product mix into the fab could exponentially increase the complexity of the scheduling problem. With cloud, you can improve your hardware – and hence your KPIs – almost immediately. Something that is extremely unlikely on-premise due to the practical implications for the IT department.
And what could be a greater incentive to become cloud-friendly than fab capacity increases of up to 10%, which is what we’ve seen using these next gen systems? That’s the type of figure which should help even the most security-conscious chip company to change their mind about cloud technology.
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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.
Meet Lio, a driving force behind client success as Flexciton's Technical Customer Lead. Discover more about her keen eye for collaboration and passion for innovation in this edition of The Flex Factor.
Meet Lio, a driving force behind client success as Flexciton's Technical Customer Lead. Discover more about her keen eye for collaboration and passion for innovation in this edition of The Flex Factor.
Tell us what you do at Flexciton?
I’m a Technical Customer Lead.
What does a typical day look like for you at Flexciton?
The day is incredibly busy and passes quickly while collaborating with the customer team and other teams at Flexciton, making rapid progress day by day. My focus revolves around ongoing customer work, such as our work at Renesas (analyzing their adherence, checking the Flex Global heat map, and listening to feedback from the client). Additionally, I often work on live demos and PoC projects. The nature of my tasks varies depending on the project stage, ranging from initial data analysis and integration to final stages where I collaborate with sales on deliverables and the story of the final report. While consistently moving forward with projects and meeting weekly targets, we concurrently establish our working methods and standardize processes to improve efficiency for future projects. For lunch, I usually go to Atis, my go-to place for fresh and nutritious meals. People in the office call it a salad, but I consider it the best healthy lunch with the highest ROI.
What do you enjoy most about your role?
I find the most enjoyment in witnessing the impact our product has on customers who need it. It's fulfilling to see their reactions when they share challenges, and I appreciate understanding how Flexciton can collaborate with them, providing that extra element for improvement.
If you could summarize working at Flexciton in 3 words, what would they be?
Creative, Fast, Collaborative.
Given the fast-paced evolution of technology, what strategies do you recommend for continuous learning and skill development in the tech field?
Stay closely connected to the client side. Understanding the technology they're developing and their current tech level (MES and other systems) provides insights into their readiness for Flexciton.
In the world of technology and innovation, what emerging trend or development excites you the most, and how do you see it shaping our industry?
The semiconductor industry's rapid evolution and diversity are fascinating. The competition between TSMC and Samsung Foundry in advanced GAA (gate-all-around) technology is particularly intriguing. While Samsung claims to be ahead, industry voices suggest a bluff with poor yields. The competition is ongoing, and I wonder if TSMC will maintain its lead or if there will be a paradigm shift in the industry.
Tell us about your best memory at Flexciton?
Meeting the Renesas team at their fab in Palm Bay and witnessing one of their operators' reaction to our app was a memorable experience. Kodi, a talented young manufacturing specialist, was genuinely impacted by our technology which was amazing to see in person. After returning home, he even had a piece of code named after him by Amar.