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 min read

Autonomous Scheduling: A Tale of Three Taxis

autonomous scheduling flexciton semiconductors industry tsmc samsung optimization fps production scheduling wafer fabs photolithography taxis new york

At Flexciton, we often talk about how autonomous scheduling allows wafer fabs to surpass the need for maintaining many rules to enable the behaviours they want at different toolsets. I would like to offer an analogy to show how significant the difference is.

Navigating the City

Imagine you are a passenger in a taxi. Your driver is a local; they know every road like the back of their hand and know the best routes to avoid likely problems. They can be flexible and effective, but have to spend a long time thinking about how to get to your destination. They also can’t know about the traffic on each potential route, and for new destinations they may require some trial and error before they find a good way of getting there. Worst of all, though they might have accumulated some great stories from their years of driving, it’s only thanks to those many years that they can navigate with any level of mastery.

Now imagine you have a very basic robotic driver; this driver is so mechanical that it has a hard-coded rule for every single road and junction: “If I’m at junction 20, I wait exactly thirty seconds and then I turn left.” This rule has come from an engineer performing a time study based on traffic levels six months ago. The driver has no knowledge of local events happening (for example, if it turns out that there is no oncoming traffic right now), and doesn’t even change its decisions when you need it to navigate to a new destination!

Meanwhile, when local conditions change at all (gaps in oncoming traffic at junction 20 are now every twenty seconds on average!) an engineer needs to manually change that parameter in the robot’s logic. And if the overall conditions change everywhere, or a new destination is desired, every rule needs to be retuned.

The autonomous taxi gets you to your destination in the shortest time possible. Meanwhile, the manual taxi gets you there slowly, and a poorly tuned robotic taxi might not get you there at all!

Finally, imagine a truly autonomous taxi. This taxi has a navigation system that knows where the traffic is, assesses the speed of every potential route, reacts to changes in conditions, and can get you to exactly where you want to go. In fact, all you have to do is tell it the destination; then you can sit back and relax, knowing it will get you there in the shortest possible time.

Navigating the Fab

While many wafer fabs have moved away from relying purely on tribal knowledge of manufacturing specialists on the fab floor, the scheduling problem in semiconductor factories is so difficult that, until recently, the hard-coded robotic taxi driver was the state-of-the-art. These solutions ask industrial engineers to manually tune thousands of rules to achieve intelligent behaviour, and they must be continuously re-tuned as fab conditions change.

A common scheduling challenge is deciding when to allow wafers into a timelink (or queue time loop) at diffusion. A timelink is the maximum amount of time that can elapse between two or more consecutive process steps, and some schedulers will simply limit the number of lots allowed within the timelinked steps at any one time. Others will just use a priority weight given to all timelinked lots, so that they are more likely to move through the loop without violating their time limit. Both of these rules are manually tuned and can’t react to the conditions of that particular moment in time, leading either to rework or scrap, or unnecessarily high cycle times.

Another typical example from a commonly-used heuristic scheduler is the application of minimum batch size rules at diffusion areas. A typical rule might be “wait for a minimum batch size of x, unless y minutes have elapsed, in which case dispatch whatever is at the rack.” Many fabs will set up this rule for every furnace-operation combination, which could mean ~3,000 manually tuned parameters just for one rule at one toolset.

Meanwhile, when micro conditions change, for example daily wip level fluctuations, these tuned parameters cannot react. And worse, when macro conditions such as overall market demand change, it makes it very hard for the whole fab to pivot quickly, because every rule needs re-tuning manually. Despite the theory that these rules can be set once every few months, in practice most fabs end up re-tuning these rules continuously, even daily, in order to maintain reasonable performance - accepting the predictable impact on industrial engineering resources that has!

Optimized scheduling, however, does away with these rules entirely and directly calculates the optimal schedule to improve your chosen objectives. In the timelink example, it doesn’t need to rely on guessing how many lots can be allowed into the loop - it just calculates the optimal schedule for the multiple steps involved, ensuring no timelink violations will occur.

But still, how do you get the scheduler to do what you want?

A New Paradigm for Tuning

If you have read any of our previous articles, you may be aware that optimization-based scheduling uses objectives such as “minimise queue time” and “maximise batch size” to calculate the optimal schedule. In fact, on most of our toolsets we only use ~2-3 objective weights, and by setting these you can achieve the balance and results you want.

Even this, however, is not truly autonomous.

We’ve been working to bring forward a new paradigm: letting you choose the fab-level outcome you want directly - like setting the destination for the taxi. If you know you want to prioritise achieving higher throughput, you can just specify that and Flexciton’s autonomous scheduler will automatically figure out what the optimization objective needs to be to achieve it.

What does this mean? It means you directly control the fab outcome you want to achieve, rather than guessing what toolset-level behaviours will produce the fab-level KPIs you want.

Orders of Magnitude

So when we speak about autonomous scheduling, we are referring to this new paradigm where you can choose the outcome you want, and Flexciton automatically does the rest. Instead of ~3,000 manually tuned parameters for just one of many rules at one toolset, just pick your desired KPI trade-off, and we automatically set the handful of objective weights that drive the optimization engine.

The result is not needing industrial engineering resource dedicated to tuning – instead, this valuable resource can be redeployed to work on higher value tasks. Moreover, it can enable consistent high performance across changes in fab conditions; and it becomes easier to pivot the entire fab’s direction when market conditions change.

This is how Flexciton’s scheduler is powerful enough to let you set the destination, and go.

Author: Sebastian Steele, Product Manager at Flexciton

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 min read
Culture
The Flex Factor with... Lio

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.

Do you think you have what it takes to work at Flexciton? Visit our careers page to browse our current openings.
flexciton semiconductor industry ai scheduling optimization job shop tsmc infineon stmicro tower vishay siemens inficon efficiency artificial intelligence machine learning reinforcement learning chatgpt
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 min read
Industry
Harnessing AI's Potential: Revolutionizing Semiconductor Manufacturing

AI has unquestionably stood out as the prevailing technological theme of the year. This wave of innovation begs the question: how can the semiconductor industry, which stands at the heart of technological progress, leverage AI to navigate its own intricate challenges?

The dominant technological theme of the year is unmistakably clear: artificial intelligence (AI) is no longer a distant future, but a transformative present. From the startling capabilities of conversational ChatGPT to the seamless navigation of autonomous vehicles, AI is demonstrating an unprecedented ability to manage complexity and enhance decision-making processes. This wave of innovation begs the question: how can the semiconductor industry, which stands at the heart of technological progress, leverage AI to navigate its own intricate challenges?

Complexity-driven Challenges 

Semiconductor wafer fabs are marvels of modern engineering, embodying a complexity that rivals any known man-made system. These intricate networks of toolsets and wafer pathways require precision and adaptability far beyond the conventional methods of management. The difficulty of this task is compounded by the current challenges that hinder its dynamic pace: a protracted shortage of skilled labor, technological advancement in product designs, and the ever-present volatility of the supply chain. 

The latest generation of products is the pinnacle of complexity, with production processes that involve thousands of steps and incredibly intricate constraints. This complexity is not just a byproduct of design; it is an inherent challenge in scaling up production while keeping costs within reasonable limits.

The semiconductor supply chain is equally complicated and often susceptible to disruptions that are becoming all too common. In this context, the requirement for skilled labor is more pronounced than ever. Running fab operations effectively demands a workforce that's not just technically skilled but also capable of innovative thinking to solve problems of competing objectives, improve processes, and extract more value. No small task in an environment already brimming with complexity.

The Need for AI in Semiconductor Manufacturing

As we delve into Industry 4.0, we find ourselves at a crossroads. The software solutions of today, while advanced, are not the panacea we once hoped for. The status quo has simply reshuffled the problems we face; we've transitioned from relying on shop floor veterans' tacit knowledge and intuition to a dependency on people who oversee and maintain the data in digital systems. These experts manning the screens are armed with MES, reporting, and legacy scheduling software, all purporting to streamline operations. Yet, the core issue remains: these systems still hinge on human intelligence to steer the intricate workings of the fabs.

At the core of these challenges lies a common denominator: the need for smarter, more efficient, and autonomous systems that can keep pace with the industry's rapid evolution. This is precisely where AI enters the frame, poised to address the shortcomings of current Industry 4.0 implementations. AI is not just an upgrade—it's a paradigm shift. It has the capability to assimilate the nuanced knowledge of experienced engineers and operators working in a fab and translate it into sophisticated, data-driven decisions. By integrating AI, we aim to break the cycle of displacement and truly solve the complex problems inherent in wafer fabs management. The potential of AI is vast, ready to ignite a revolution in efficiency and strategy that could reshape the very fabric of manufacturing.

Building AI for the Semiconductor Industry

Flexciton is the first company that built an AI-driven scheduling solution on the back of many years of scientific research and successfully implemented it into the semiconductor production environment.  So how did we do it?  

Accessing the Data 

The foundation lies in data – clean, accessible, and comprehensive data. Much like the skilled engineers who intuitively navigate the fab's labyrinth, AI requires a map – a dataset that captures the myriad variables and unpredictable nature of semiconductor manufacturing. 

Despite the availability of necessary data within fabs, it often remains locked in silos or relegated to external data warehouses, making it difficult to access. Yet, partnerships with existing vendors can unlock these valuable data reserves for AI applications.

Finding People Who Can Build AI

The chips that enable AI are designed and produced by the semiconductor industry, but the AI-driven applications are developed by people who are not typically found within the sector. They align with powerhouses like Google and Amazon or deep-tech companies working on future-proof technologies. This reveals a broader trend: the allure of semiconductors has diminished for the emerging STEM talent pool, overshadowed by the glow of places where state-of-the-art tech is being built. Embracing this drift, Flexciton planted its roots in London, a nexus of technological evolution akin to Silicon Valley. This strategic choice has enabled us to assemble a diverse and exceptional team of optimization and software engineers representing 22 nationalities among just 43 members. It's a testament to our commitment to recruiting premier global talent to lead the charge in tech development, aiming to revolutionize semiconductor manufacturing. 

AI Needs Cloud

The advent of cloud computing marks a significant milestone in technological evolution, enabling the development and democratization of technology based on artificial intelligence. At the core of AI development lies the need for vast computing power and extensive data storage capabilities. The cloud environment offers the ability to rapidly provision resources at a relatively low cost. With just a few clicks, a new server can be initialized, bypassing the traditional complexities of hardware installation and maintenance typically handled by IT personnel.

Furthermore, the inherent scalability of the cloud means that not only can we meet our current computing needs but we can also seamlessly expand our resources as new technologies emerge. This flexibility provides collaborating fabs with the latest technology while avoiding the pitfalls of significant initial investment in equipment that requires regular maintenance and eventually becomes obsolete.

Security within the cloud is an area where misconceptions abound. As a cloud-first company, we often address queries about data security. It's crucial to understand that being cloud-first does not equate to possessing your data. In fact, your data is securely stored in Microsoft Azure data centers, which are bastions of security. Microsoft's commitment to cyber security is reflected in its employment of more than 3,500 professionals whose job is to ensure that data centers are robust and a fortress for data, offering peace of mind that often surpasses the security capabilities of private data centers.

Effective Deployment of AI in Fabs

The introduction of AI-driven solutions within a fab environment entails a significant change in existing processes and workflows and often results in decision-making that diverges from the traditional. This can unsettle teams and requires a comprehensive change management strategy. Therefore the implementation process must be planned as a multifaceted endeavor and deeply rooted in human collaboration. 

A successful deployment begins with assembling the right team—a blend of industrial engineers with intimate knowledge of fab operations, and technology specialists who underpin the AI infrastructure. This collective must not only include fab management and engineers but also those who are the lifeblood of the shop floor—individuals who intimately understand the fab's heartbeat.

When it comes to actual deployment, the process is iterative and data-centric. Setting clear objectives is pivotal. The AI must be attuned to the Fab's goals—be it enhancing throughput or minimizing cycle times. Often, the first output may not align with operational realities—a clear indication of the AI adage that the quality of input data dictates the quality of output. It is at this juncture that the expertise of Fab professionals becomes crucial, scrutinizing and correcting the data, and refining the schedules until they align with practical Fab dynamics. With objectives in place and a live scheduler operational, the system undergoes rigorous in-FAB testing.

Change management is the lynchpin in this transformative phase. The core of successful AI adoption is rooted in the project team's ability to communicate the 'why' and 'how'—to educate, validate, and elucidate the benefits of AI decisions that, while novel, better align with overarching business goals and drive performance metrics forward.

Making AI Understandable and Manageable

The aversion to the enigmatic 'black box' is universal. In the world of fabs, it can be a barrier to trust and adoption —operational teams must feel empowered to both grasp and guide the underlying mechanisms of AI models.

We made a considerable effort to refine our AI scheduler by incorporating a feature that enables the user to influence the objective of what our AI scheduler is tasked to achieve and also to understand the decision. Once a schedule is created, engineers can look through those decisions and inspect and interrogate them to understand why the scheduler made these decisions.

Case Studies: Success Stories of AI Deployment

I firmly believe that we are on the cusp of a transformative era in semiconductor manufacturing, one where AI-driven solutions will yield unprecedented benefits. To illustrate this, let's delve into some practical case studies. 

The first involves implementing Flexciton's AI scheduler within the complex diffusion area of a wafer fab—a zone notorious for its intricate processes. We aimed to achieve a trifecta of goals: reducing batch sizes, minimizing rework, and significantly reducing reliance on shop floor decision-making. The challenge was magnified by the fab's limited IT and IE resources at the time of deployment. Partnering with an existing vendor whose systems were already integrated and had immediate access to essential data facilitated a rapid and efficient implementation with minimal engagement of the fab's IT team. This deployment led to remarkable improvements: clean tools saw 25% bigger batches, and rework in the diffusion area was slashed by 36%.

Another case study details a full fab deployment, where the existing rules-based scheduling system was replaced with Flexciton's AI scheduler. The goal was to enhance capacity and reduce cycle times. The deployment was staged, beginning with simpler areas starting with metrology tools, through the photolithography area and eventually scaling to the entire fab, yielding a global optimization of work-in-process (WIP) flow. The result was a significant increase in throughput and a staggering 75% reduction in manual flow control transactions, a testament to the AI's ability to autonomously optimize WIP flow and streamline operations.

The Autonomous Future of Semiconductor Manufacturing

In closing, the semiconductor industry stands on the precipice of a new era marked by autonomy. AI technology, with its capacity to make informed decisions without human input, has demonstrated not only the potential for improved KPIs but also a significant reduction in the need for human decision-making. The future of semiconductor manufacturing is one where AI-driven solutions consistently deliver superior production results, alleviating the human workload and steering fabs towards their objectives with unprecedented precision and efficiency.

As we embrace this autonomous future, it becomes clear that the integration of AI in semiconductor manufacturing is not just an enhancement of the status quo but a reinvention of it. With each fab that turns to AI, the industry moves closer to realizing a vision where technology and human ingenuity converge to create a landscape of limitless potential.

Author: Jamie Potter, CEO and Cofounder, Flexciton

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Read time
 min read
Culture
The Flex Factor with... Will

Introducing Will, Lead Backend Engineer at Flexciton. Explore his daily tasks, ranging from crafting backend architecture to overseeing the codebase and managing technical debt in this month's edition of The Flex Factor.

Introducing Will, Lead Backend Engineer at Flexciton. Explore his daily tasks, ranging from crafting backend architecture to overseeing the codebase and managing technical debt in this month's edition of The Flex Factor.

Tell us what you do at Flexciton?

I am a lead backend engineer and the software development practice lead. My work involves designing the backend architecture, managing the codebase structure and technical debt, pushing for best practices across the wider engineering team and contributing features to my delivery team.

What does a typical day look like for you at Flexciton?

I usually start my morning by scanning through the production logs from our deployments and seeing if anything looks suspect and in need of an investigation. From there it will depend on what I am focused on for that week which tends to vary a fair amount. The majority of my time is spent coding features or doing large scale design work. Some days I get to spend refactoring and restructuring our codebase, occasionally I will get to work in the devops or optimisation space which I always look forward to. In any given week there will be a handful of ongoing projects at various stages, from architectural designs to software development practice work that needs to be structured and prioritised. No day goes by without me writing at least some code, but there is a fair amount of admin work to do as well.

What do you enjoy most about your role?

The diversity of the work I get to do. My work often overlaps with optimisation and devops so I can find myself speaking the lots of different people throughout the day. There are many opportunities to dive deeper into a topic with various team members willing to support you. Since joining I have worked with terraform, CI pipelines, infrastructure, hardware configuration, optimisation, frontend, customer deployments, database optimisation and management, the application backend and much more.

If you could summarise working at Flexciton in 3 words, what would they be?

Collaborative, Challenging, Diverse.

What emerging technology do you believe will have the biggest impact on our lives in the next decade?

I think the next decade is going to be made great by lots of smaller contributions made across technology from both hardware and software. I don’t have much hope for AGI / useful AGI this decade but there is a lot going on to be excited about. From a hardware perspective we have companies making huge progress in designing chips specifically for model training, and at the other end of the spectrum more companies are putting satellites into orbit to enable global access to high speed internet. AI has fuelled the search in identifying stable structures for proteins and crystals, pushing frontiers of new medicines and treatments, as well as material science. Memory safety in programming languages has started to draw attention from governments too with languages like Rust (and potentially Hylo in the future) likely to lead for memory safe applications. It will be interesting to see how the landscape changes over the next few years and see companies start to shift their codebases over.

What’s the best piece of advice you’d give to someone starting a career in the tech industry today?

I think the best piece of advice would be to throw away any notion of imposter syndrome from the start. Programming, and tech in general, is massive, and its certainly true that the more you know, the more you realise you do not know. Everyone will take a different path throughout their career and find themselves being expert in one topic and (momentarily) hopeless in another. When the topics that you know nothing about come along, its best to embrace that and start finding opportunities to learn. It is important to convince yourself that while you may not be able to learn everything, you could learn anything and find joy in accruing that knowledge as you progress in your career. Bearing this in mind, I would say come into tech because you love it and because you want to learn. There is such as good community across programming languages and industries, anyone who wants to learn can easily find help.

Tell us about your best memory at Flexciton?

I can’t think of one great memory that stands out, but what makes Flexciton great is all the little things that happen week after week such that by Sunday evening, I am looking forward to speaking with my team in Monday standup.