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Multi-objective Fab Scheduling: Exploring Scenarios and Tradeoffs for Better Decision Making

multi objective optimization for wafer fab scheduling tradeoffs

Building and maintaining any form of scheduling solution to be flexible yet robust is not an easy undertaking. Commonly, fab managers have resorted to rule-based dispatch systems or other discrete-event simulation software to estimate how their fab will play out in the near future. Often this requires deciding a specific KPI that is important to the fab up-front; do I care more about getting wafers out the door, or reducing the cycle time of those wafers?

Competing objectives challenge

As a fab manager, there are a number of competing objectives to balance on the shop floor that all impact the profitability of the fab. Whether that be reliably delivering to customers their contractual quantities on time, or ensuring that fab research and development iteration time is kept low, fabs need a flexible, configurable scheduling solution that can produce a variety of schedules which account for these tradeoffs. At Flexciton, we call this “multi-objective” scheduling; optimizing the factory plan whilst considering several independent KPIs that, in this case, are fundamentally at odds with one another. This article explores Flexciton’s approach to multi-objective scheduling and how we expose simple configurations to the fab manager, whilst allowing our scheduling engine to ultimately decide on how that configuration plays out in the fab.

If there is no automated real-time dispatch system in the fab, determining the "best" schedule is a very complex procedure that cannot even be accomplished with advanced spreadsheet models. Assuming that the fab is advanced enough such that a dispatch system is in place, it will likely only consider "local" decisions pertaining to the lots that are immediately available to the dispatch system at the time the decision is made.

Dispatch systems typically do not have the configurability to adjust the user's incremental utility with respect to throughput and cycle time; they typically adhere to a series or hierarchy of rules that are tuned to consider exactly one KPI. Therefore to change the objective of the dispatch system would require rewriting these rules; an often time-consuming exercise that requires advanced technical knowledge of the dispatch system. This makes it almost impossible or otherwise very time consuming to trial various configurations of the fab manager’s preferences.

Balancing various objectives for best results

The Flexciton optimization engine is a multi-objective solution that can linearly balance various KPIs according to user-chosen weights. As these weights are exposed to the end-user, this renders the possibility of running many different scenarios with varying preferences trivial. Fab managers can have access to the specific weight values themselves or work with our expert optimization engineers to select from a handful of high-level configurations and the solution will select appropriate weights itself.

To properly understand the flexibility of the engine, we will now step through four case studies. The goal is to compare how, given the same dataset, slightly different objective configurations impact the solution that is returned by accounting for the change in preferences.

We present a schedule of nine tools from across five toolsets with seventy lots of a mix of 65% Priority1 lots. Each lot can go to a random subset of tools within a single toolset.

The schedule will then be tested against four runs:

  1. Produced by a dispatch system with heuristic rules
  2. Optimized for cycle time
  3. Optimized for the on-time delivery of wafers
  4. Balanced optimization considering both cycle time and OTD

For each of these scenarios, we will present two gantt charts; one labelled with the “Queueing Time” of each lot (aka “rack time”) and another labelled with the “Late Time” of each lot. Late time refers to the duration by which the lot completed processing after its due date. If it was not late, the label reads “0s” since we do not consider being more early as being more favourable. Lots that are considered high priority (Priority 1 to 3) are given a circle badge indicating such. Low priority lots are Priority 4 through 10. Each lot is coloured according to this priority class.

Case study #1: base case - greedy dispatch

To begin, we’ll present how a schedule could look when produced by a dispatch heuristic that does not consider the future arrivals of wafers, but simply what is currently available in front of a tool. The greedy rule here is to just dispatch the highest priority wafer on the rack at the point the tool is idle.

In the above example, the high-priority wafers have to wait due to the system only considering what’s on the rack and therefore dispatching the low-priority wafers that are ready to go.

It should be noted that such a strategy is great for improving overall throughput and cycle time since the machine idle time is reduced by constantly dispatching wafers. This has the side effect of delivering all-bar-one of the wafers on time. In reality though, not all lots are equal and fab managers care a great deal more about certain high-priority lots thus making the scheduling problem quite a bit trickier.

Unfortunately, in order to reconfigure the system to place greater importance upon the high-priority wafers and dispatch them first would require complex rewriting of the dispatch  rules to “look ahead” at the wafers that are not yet on the rack, and are arriving shortly. The dispatcher would then elect to keep the machine idle in order to reduce the high-priority wafer cycle time.

Case study #2: Optimize for high-priority-lot cycle time

Instead of modifying the RTD rules, we can emulate what that would look like by running our optimization engine whilst optimizing for the cycle time of high-priority lots:

The low priority lots at the front of the schedule are replaced with high-priority lots so that they can be dispatched as soon as they arrive. These low priority lots have been pushed to the back of the schedule with non-zero rack time (since the cycle time of high priority lots matters so much more). Naturally this is at the cost of overall average cycle time which has suffered by 23% in order to improve Priority1 cycle time by 11%. Also note that on tool “SBXF/115”, our scheduling solution has pushed the Priority2 (orange) and the Priority10 (green) lots later so that the Priority1 (red) lots are rushed through with zero rack time.

Case study #3: Optimize for on-time delivery

With optimisation, there are no additional changes required to increase the flexibility of the system. We simply describe what a good schedule looks like using the multi-objective function and the optimizer does the rest. Subtle tweaks to this function will inevitably produce very different schedules. Now let’s take a look at how the schedule alters when we want to maximise solely on-time delivery.

As expected, cycle time is quite a bit worse than previously however now there are no lots delivered late. This is very similar to the original schedule produced by simple dispatch rules. The low-priority lots have been brought forward so that they are delivered on time and the cycle time of the high-priority lots suffer as a result.

Case study #4: Optimize for both

Finally, the main purpose of this article is to illustrate the ease of considering both KPIs with some relative weight simultaneously.

Note that the KPIs of cycle time and throughput are slightly worse than when that was the sole KPI being optimised. The key is that both are better than when the other KPI was being optimized. This balance is entirely in the hands of the fab manager. We maintain roughly the same cycle time of high-priority lots as when optimising for cycle time and fewer lots are late than when optimizing only cycle time.

Summary and Conclusions

This article has provided a number of ways that illustrate how optimization can be considered both more flexible and robust than heuristics that cannot effectively search the global solution space.

The engine is simple to tune due to the exposed weights and/or configurations presented to the fab manager which allow a high degree of customisation both with respect to the objective function and wafer priorities. This flexibility allows us to easily consider complex hierarchical objectives found in semiconductor manufacturing such as “optimise high-priority cycle time as long as no P1-8 lots are late” or “optimise batching efficiency (perhaps due to operator constraints) and then high-priority cycle time”. Ultimately, our solution is a market-leading scheduler that will realise true KPI improvements on your live wafer fabrication data.

Flexciton is currently offering the Fab Scheduling Audit free of charge. To enquire, please click here.

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Switching to Autonomous Scheduling: What is the Impact on Your Fab?

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. 


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.

Coming soon: our new Autonomous Scheduling Technology White Paper

We will soon be releasing a White Paper on Autonomous Scheduling Technology (AST) with insights into the latest advancements and benefits.

Click here to be the first to receive it upon release. 

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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.
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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: maximize batch sizes, minimize rework, and significantly reduce 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