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A is for AI

artificial intelligence ai AI optimization flexciton semiconductors wafer fabs semiconductor production scheduling efficiency productivity machine learning reinforcement technology elon musk samsung tsmc

We are excited to introduce the Flexciton Tech Glossary Blog Series: A Deep Dive into Semiconductor Technology and Innovation.

In an ever-evolving semiconductor industry, understanding the nuances of new technologies and the transformative potential of artificial intelligence and optimization is paramount. The Flexciton Tech Glossary Blog Series is designed to shed light on specific technologies and innovations, offering insights into how these advancements can revolutionise semiconductor manufacturing operations.

Each article in this series will delve into a distinct theme, aiming to equip any practitioner in the industry from industrial engineers and manufacturing experts up to VP level professionals with the knowledge to integrate these innovations into their daily operations.

Beyond our in-house expertise, we’re excited to collaborate with industry experts, inviting them to contribute and enrich our series with their specialised knowledge and experience. Join us on this enlightening journey as we explore the frontiers of the semiconductor industry from A-Z.

AI will transform the semiconductor industry

Artificial Intelligence (AI) has become a transformative force in various sectors, driving a global wave of innovation and automation. Seemingly overnight, systems like ChatGPT that harness the primary human interface – natural language – have revolutionised how we interact with technology. In a similar vein, generative art technologies have reinvented our relationship with creativity, making it more accessible than ever before. These remarkable systems have acquired their capabilities through learning, fueled by training on vast amounts of data. This ongoing revolution prompts the question: what is the next frontier to be conquered?

Beyond the novel consumer applications leading the charge, the implications of AI in specialised fields, such as semiconductor manufacturing, are equally profound. Estimates place the earnings already achieved by AI across the semiconductor value chain at over $5 billion. The range of applications is immense and spans activities at all levels. From informing capital allocation, to demand forecasting, fab layout planning, and right down to chip design, AI can enable automation and increase efficiency. Semiconductor manufacturing, in particular, has been identified as the function presenting the most attractive opportunities, where the potential savings have been calculated to be over $10 billion in just the next few years [1].

Impact at all levels 

The semiconductor industry is facing several challenges where AI can make a significant impact. These span all the industry’s key activities: long-term capacity planning, research & design, sales, procurement and, of course, manufacturing. Some use cases that are increasingly gaining traction are:

  • Supply Chain Optimization: Predictive analytics can forecast demand, optimize inventory levels, and enhance the overall efficiency of the supply chain [2]. 
  • Automated Material Handling Systems (AMHS): Utilising AI-driven cognitive robotics within AMHS automates material transportation throughout the plant [3], optimizing production planning considering AMHS [4].
  • Predictive Maintenance: AI can predict when equipment is likely to fail or require maintenance, reducing downtime and increasing overall equipment efficiency [5].
  • Defect Detection: Advanced image recognition algorithms can identify defects in wafers at an early stage, ensuring higher yields and reducing wastage [6].
  • Virtual metrology: AI can be deployed to estimate a product’s quality directly from production process data. This enables real-time quality monitoring without additional measuring steps [7]. 
  • Process control: AI can analyse vast amounts of data to optimize the manufacturing process, ensuring the best conditions for each step and improving the overall quality of the chips (e.g. tool matching) [8].

An automated material handling system (AMHS) inside the clean room of a wafer fab.

In this article, we focus on AI’s potential to automate scheduling within a semiconductor wafer fab and improve key metrics: increase the throughput of manufacturing lines, reduce cycle times and improve on-time delivery. But first, we step back and define both intelligence and artificial intelligence. 

Defining intelligence

Defining intelligence has been a long-standing challenge, with various perspectives offered. A widely-accepted definition, which broadly aligns with the context of semiconductor applications, is as follows:

Intelligence is the ability to accomplish complex goals. 

As suggested by Max Tegmark [9], intelligence is not universal but depends on the defined goal. As such, there are many possible types of intelligence. Extending this concept further, intelligence can be characterised according to the following features.

Goal type: Intelligence can be technical (problem-solving), social (interaction), or creative (idea generation).

Skill level: This is typically categorised as below/equivalent/super-human level. This determines whether we aim to match the performance of a human or surpass it.

Scope: Narrow intelligence specialises in a specific task, while broad intelligence encompasses a wide range of tasks like human intelligence.

Autonomy: Intelligence can operate with varying degrees of independence, from human-guided to fully autonomous.

In semiconductor scheduling, super-human performance level is necessary to sift through billions or even trillions of candidate solutions to derive optimal decisions, whilst adhering to complex constraints. Focusing on the narrow scope of scheduling allows the system to specialise, thereby optimizing its performance for these specific requirements. The technical nature of the task calls for a solution that exploits the strictly technical aspects to achieve superhuman performance. Finally, a system with high autonomy and no need for human intervention is desired in such a dynamic environment. 

Three important facets of Artificial Intelligence

AI involves creating models and machines that mimic human intelligence, including learning, reasoning, and decision-making. 

Learning is an important aspect of AI, relying on a model’s ability to iteratively refine its internal parameters until it can accurately capture underlying patterns. Machine Learning is the cornerstone approach for learning from data and techniques in this category can range from simple models like Linear Regression to complex Deep Learning networks. 

Reasoning involves drawing inferences based on established rules and facts, mimicking the human ability to logically connect information. It can aid in tasks like medical diagnosis (See the generative LLM AI from Google Med-Palm 2) or legal case analysis.  

Decision-making encompasses action exploration and problem-solving. Action exploration deals with determining actions through interaction with an environment, which can vary from well-defined scenarios, like a chess game, to unstructured situations, like driving a car. Problem-solving, on the other hand, focuses on finding solutions to clearly defined problems with specific objectives and constraints. This can involve simple tasks like sorting or more intricate challenges such as route planning, resource allocation, and scheduling. Optimization and mathematical programming are often employed in these contexts.

Five Crucial Factors When Selecting AI for Production Scheduling

Production scheduling involves making optimal choices to coordinate resources, tasks, and time to meet production goals. It requires handling well-defined parameters and constraints, along with specific objectives like maximising throughput or achieving on-time delivery. As such, it is best suited to rigorous and well-structured AI methods that focus on optimal and feasible decision-making such as mathematical programming

Nevertheless, good production scheduling can involve some aspects of learning and reasoning as well. Learning can be useful when some of the parameters are not well defined or static. For example, estimating transfer times between different locations of a fab may depend on various parameters, necessitating the use of a prediction model that has learned from past data. In terms of reasoning, a good decision-making approach should allow some degree of introspection from the user. Contrary to black box approaches, such as deep neural networks, mathematically formal methods such as Mixed Integer Linear Programming (MILP) enable transparency and explainability.

Choosing the right AI technique for production scheduling in semiconductor manufacturing involves navigating the intricate balance among five crucial characteristics, each vital in this high-stakes field:

Optimality refers to the ability of an AI technique to reach and prove that the true optimal solution has been found. In a complex environment such as a semiconductor fab, where small improvements can have significant cost or time implications, optimality is of paramount importance. 

Feasibility is about ensuring that the solution found truly abides by the constraints of the problem. Semiconductor fabs are bounded by many constraints, including machine capacity, human resources, and time windows. An AI solution must respect these constraints while optimizing the schedule. 

Speed is crucial as it directly impacts the responsiveness of the system. Semiconductor manufacturing is a dynamic environment with constantly changing states. Therefore, the selected AI technique must be able to provide fast and accurate solutions to adapt to these changing conditions. 

Explainability refers to the ability of an AI technique to provide insights into how it arrived at a given solution. In a high-stakes environment like a semiconductor fab, explainability helps build trust in the system, enables troubleshooting, and allows for more effective human-AI collaboration.

Flexibility refers to the technique’s applicability across a wide range of possible scenarios and system changes. This attribute highlights the capability of an AI method to be fully autonomous and require  minimal human supervision and intervention. Within the context of a semiconductor plant, this quality is indispensable, especially as complexity grows and specialised personnel are spread thinner across other functions. 

Different AI techniques fare differently on these dimensions. Rule-based systems offer high explainability and feasibility but may lack optimality, especially in complex scenarios. Unforeseen changes in a fab’s state may require rule adjustments or even entirely new ones, affecting flexibility. Heuristic approaches can provide acceptable solutions quickly, but typically cannot provide optimality or feasibility guarantees. Reinforcement learning can potentially offer high levels of optimality and speed, but at the cost of explainability, the risk of infeasibility, and the need for extensive tuning. 

In contrast, mathematical programming techniques, such as MILP, can offer an excellent balance. They provide guaranteed feasibility, while the distance to true optimality can be easily computed. They offer explainability in terms of how decisions are made based on the objective function and constraints. Although computational complexity can be an issue, they can greatly benefit from advanced decomposition methods, and are well complemented by heuristic methods [10].

In the context of semiconductor fab scheduling, where feasibility, optimality, and explainability are particularly important, mathematical programming techniques can be a superior choice for AI implementation. Their deterministic nature and the rigour of their mathematical foundations make them a highly reliable and robust choice for such high-stakes, complex operational problems.

Going beyond with AI

Today, AI in semiconductor manufacturing stands at a critical point. With the increasing complexity of semiconductor processes and the escalating demand for efficiency and quality, the need for effective AI solutions has never been greater. As evidenced in many large companies’ roadmaps, AI is regarded as a key enabling technology of the future [11]. Companies that do not devote resources to a comprehensive AI strategy risk being left behind.

As we delve deeper into the era of AI-driven manufacturing, the nuanced roles of different AI techniques will become more and more apparent. Machine learning approaches bring novel capabilities for learning and predicting from data: yield improvement and predictive maintenance are very promising paths. When it comes to autonomously and reliably scheduling and planning operations in a fab, an exact optimization approach, such as MILP, becomes the key to unlocking peak performance.

Ioannis Konstantelos, Principal Optimization Engineer at Flexciton
Dennis Xenos, CTO and Cofounder at Flexciton


[1] McKinsey & Company, Scaling AI in the sector that enables it: Lessons for semiconductor-device makers, April 2021. Link

[2] Mönch, L., Uzsoy, R. and Fowler, J.W., 2018. A survey of semiconductor supply chain models part I: semiconductor supply chains, strategic network design, and supply chain simulation. International Journal of Production Research, 56(13), pp.4524-4545.

[3] Lee, T.E., Kim, H.J. and Yu, T.S., 2023. Semiconductor manufacturing automation. In Springer Handbook of Automation (pp. 841-863). Cham: Springer International Publishing.

[4] Mehrdad Mohammadi, Stephane Dauzeres-Peres, Claude Yugma, Maryam Karimi-Mamaghan, 2020, A queue-based aggregation approach for performance evaluation of a production system with AMHS, Computers & Operations Research, Vol. 115, 104838, https://doi.org/10.1016/j.cor.2019.104838

[5] Çınar, Z.M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M. and Safaei, B., 2020. Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), p.8211.

[6] Ishida, T., Nitta, I., Fukuda, D. and Kanazawa, Y., 2019, March. Deep learning-based wafer-map failure pattern recognition framework. In 20th International Symposium on Quality Electronic Design (ISQED) (pp. 291-297). IEEE.

[7] Dreyfus, P.A., Psarommatis, F., May, G. and Kiritsis, D., 2022. Virtual metrology as an approach for product quality estimation in Industry 4.0: a systematic review and integrative conceptual framework. International Journal of Production Research, 60(2), pp.742-765.

[8] Moyne, J., Samantaray, J. and Armacost, M., 2016. Big data capabilities applied to semiconductor manufacturing advanced process control. IEEE transactions on semiconductor manufacturing, 29(4), pp.283-291.

[9] Max Tegmark, Life 3.0, Being human in the age of Artificial Intelligence, 2018 

[10] S. Elaoud, R. Williamson, B. E. Sanli and D. Xenos, "Multi-Objective Parallel Batch Scheduling In Wafer Fabs With Job Timelink Constraints," 2021 Winter Simulation Conference (WSC), Phoenix, AZ, USA, 2021, pp. 1-11, doi: 10.1109/WSC52266.2021.9715465.

[11] Bosch, Humans and machines team up in the factory of the future, October 2021. Link

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The Flex Factor with... Jannik

Please give a warm welcome to Jannik, our next team member to sit in the hot seat. In this edition of The Flex Factor, find out how Jannik juggles being both an optimization engineer and customer lead, as well as what get's him excited in the world of tech.

Please give a warm welcome to Jannik, our next team member to sit in the hot seat. In this edition of The Flex Factor, find out how Jannik juggles being both an optimization engineer and customer lead, as well as what get's him excited in the world of tech.

Tell us what you do at Flexciton?

I’m an optimization engineer and technical customer lead working in the customer team. As an optimization engineer, I work on our models and the general back-end code to make sure we create optimal schedules that meet the client’s requirements.

As a customer lead, I speak to our clients to understand their unique challenges, so that I can translate them into requirements for our solution and liaise with our team to prioritise the right bits of work we want to get done.

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

To start my day I like to have a check in with my clients, to make sure their apps are working as expected and there are no queries waiting to be handled. Other than that, there is no such thing as a typical day.

Some days will be full of programming to create solutions for new problems we encounter, or to iron out bugs that made their way into the code during previous work. Other days might have lots of meetings to align our work with the engineering & product teams, or to speak with our customers and technology partners.

What do you enjoy most about your role?

My role has loads of connections within the company, which means I get to work with many super smart people to achieve our goals. I also really enjoy learning about the many different challenges our clients face and create solutions for them, and occasionally I get to visit clients and peek inside the cleanroom, which never fails to amaze me.

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

Challenges, curiosity, intelligence.

If you could have dinner with any historical figure, living or deceased, who would it be, and why?

Sebastião Salgado, the Brazilian photographer. Not only is he an inspirational photographer, he must also be full of stories and life lessons from many years of travelling and reforesting his family's farm land.

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?

It’s a very broad trend, but it’s amazing to see AI solutions spreading to more and more people and helping them in their daily lives. You’d think an industry like semiconductors is at the forefront of this, but we can see that there is still a lot of hidden potential which we can hopefully help to unlock over the next few years by replacing some of the legacy technology.

Tell us about your best memory at Flexciton?

This one is really tough because I love all the small moments here, from having a super technical discussion amongst engineers to finding out a new fun fact about each other over some drinks.

If I have to pick a single moment, it would be our surfing lesson near Albufeira during last year’s team trip. It was just loads of fun trying it out (and failing) together.

We're hiring! To see what vacancies we have available, check out our careers site.

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B is for Batching

In the second instalment of the Flexciton Tech Glossary Series, we're taking you on an insightful journey through the world of batching. Find out about the many complexities of batching, the existing methods of solving the problem and the wider solution space.

Welcome back to the Flexciton Tech Glossary Series: A Deep Dive into Semiconductor Technology and Innovation. Our second entry of the series is all about Batching. Let's get started!

A source of variability

Let's begin with the basics: what exactly is a batch? In wafer fabrication, a wafer batch is a group of wafers that are processed (or transported) together. Efficiently forming batches is a common challenge in fabs. While both logistics and processing both wrestle with this issue, our article will focus on batching for processing, which can be either simultaneous or sequential.

Figure 1: the different types of batching in a wafer fab.

Simultaneous batching is when wafers are processed at the same time on the same machine. It is very much inherent to the entire industry, as most of the machines are designed for handling lots of 25 wafers. There are also process types – such as thermal processing (e.g. diffusion, oxidation & annealing), certain deposition processes, and wet processes (e.g. cleaning) – that benefit from running multiple lots in parallel. All of these processes get higher uniformity and machine efficiency from simultaneous batching.

On the other hand, sequential batching refers to the practice of grouping lots or wafers for processing in a specific order to minimise setup changes on a machine. This method aims to maximise Overall Equipment Effectiveness (OEE) by reducing the frequency of setup adjustments needed when transitioning between different production runs. Examples in wafer fabrication include implant, photolithography (photo), and etch. 

Essentially, the entire process flow in wafer manufacturing has to deal with batching processes. To give a rough idea: a typical complementary metal-oxide semiconductor (CMOS) architecture in the front-end of the line has up to 70% of value added steps being batching. In a recent poll launched by FabTime on what the top cycle time contributors are, the community placed batching at number 5[1], behind tool downs, tool utilisation, holds, and one-of-a-kind tools. Batching creates lot departures in bursts, and hence it is inherently causing variability in arrivals downstream. Factory Physics states that:

“In a line where releases are independent of completions, variability early in a routing increases cycle time more than equivalent variability later in the routing.” [2]

Successfully controlling this source of variability will inevitably result in smoother running down the line. However, trying to reduce variability in arrival rates downstream can lead to smaller batch sizes or shorter campaign lengths, affecting the effectiveness of the batching machines themselves.

The many complexities of batching

In wafer fabs, and even so more in those with high product mix, batching is particularly complicated. As described in Factory Physics:

"In simultaneous batching, the basic trade-off is between effective capacity utilisation, for which we want large batches, and minimal wait to batch time, for which we want small batches.” [2]

For sequential batching, changing over to a different setup of the machine will cause the new arriving lots to wait until the required setup is available again.

So in both cases, we’re talking about a decision to wait or not to wait. The problem can easily be expressed mathematically if we’re dealing with single product manufacturing and a low number of machines to schedule. However, as one can imagine, the higher the product mix, the more possible setups and machines. Then the problem complexity increases, and the size of the solution space explodes. That’s not all, there are other factors that might come into play and complicate things even more. A couple of examples are:

  • Timelinks or queue time constraints: a maximum time in between processing steps
  • High-priority lots: those that need to move faster through the line for any reason
  • Downstream capacity constraints: machines that should not get starved at any cost
  • Pattern matching: when the sequence of batching processes need to match a predefined pattern, such as AABBB

Strategies to deal with batching

Historically, the industry has used policies for batching; common rules of thumb that could essentially be split up into ‘greedy’ or ‘full batch’ policies[3]. Full batch policies require lots to wait until a full batch is available. They tend to favour effective capacity utilisation and cost factors, while they negatively impact cycle time and variability. Greedy policies don’t wait for full batches and favour cycle time. They assume that when utilisation levels are high, there will be enough WIP to make full batches anyway. For sequential batching on machines with setups, common rules include minimum and maximum campaign length, which have their own counterpart configurations for greedy vs full batching.[3] 

The batching formation required in sequential or simultaneous batching involves far more complex decisions than that of loading a single lot into a tool, as it necessitates determining which lots can be grouped together. Compatibility between lots must be considered, and practitioners must also optimize the timing for existing lots on the rack to await new arrivals, all with the goal of maximising batch size. [4]

Figure 2: Impact of Greedy vs. Near-full batch policy on cycle time x-factor for a tool. [4]

Industrial engineers face the challenge of deciding the best strategy to use for loading batch tools, such as those in the diffusion area. In an article by FabTime [4], [5] the impact of the aforementioned greedy vs full or near full batch policy is compared. The greedy heuristic reduces queuing time and variability but may not be cost-effective. Full batching is cost-effective but can be problematic when operational parameters change. For instance, if a tool's load decreases (becomes less of a bottleneck), a full batch policy may increase cycle time and overall fab variability. On the other hand, a greedy approach might cause delays for individual lots arriving just after a batch is loaded, especially critical or hot lots with narrow timelink windows. Adapting these rules to changing fab conditions is essential.

In reality, these two policies are two extreme settings in a spectrum of possible trade-offs between cost and cycle time (and sometimes quality). To address the limitations of both the greedy and full batch policies, a middle-ground approach exists. It involves establishing minimum batch size rules and waiting for a set duration, X minutes, until a minimum of Y lots are ready for batching. This solution usually lacks robustness because the X and Y values depend on various operational parameters, different recipes, product mix, and WIP level. As this rule-based approach incorporates more parameters, it demands greater manual adjustments when fab/tool settings change, inevitably leading to suboptimal tool performance.

In all of the above solutions, timelink constraints are not taken into consideration. To address this, Sebastian Knopp[6] recently developed an advanced heuristic based on disjunctive graph representation. The model's primary aim was to diminish the problem size while incorporating timelink constraints. The approach successfully tackled real-life industrial cases but of an unknown problem size.

Over the years, the wafer manufacturing industry has come up with various methodologies to help deal with the situation above, but they give no guarantee that the eventual policy is anywhere near optimal and their rules tend to stay as-is without adjusting to new situations. At times, this rigidity has been addressed using simulation software, enabling factories to experiment with various batching policy configurations. However, this approach proved to be resource-intensive and repetitive, with no guarantee of achieving optimal results.

How optimization can help master the batching problem

Optimization is the key to avoiding the inherent rigidity and unresponsiveness of heuristic approaches, helping to effectively address the batching problem. An optimization-based solution takes into account all batching constraints, including timelinks, and determines the ideal balance between batching cost and cycle time, simultaneously optimizing both objectives.

It can decide how long to wait for the next lots, considering the accumulating queuing time of the current lots and the predicted time for new lots to arrive. No predetermined rules are in place; instead, the mathematical formulation encompasses all possible solutions. With a user-defined objective function featuring customised weights, an optimization solver autonomously identifies the optimal trade-off, eliminating the need for manual intervention.

The challenge with traditional optimization-based solutions is the computational time when the size and complexity of the problem increase. In an article by Mason et al.[7], an optimization-based solution is compared to heuristics. While optimization outperforms heuristics in smaller-scale problems, its performance diminishes as problem size increases. Notably, these examples did not account for timelink constraints.

This tells us that the best practice is to try to break down the overall problem into smaller problems and use optimization to maximise the benefit. At Flexciton, advanced decomposition techniques are used to break down the problem to find a good trade-off between reduced optimality from the original problem and dealing with NP-hard complexity.[8]

Many practitioners aspire to attain optimal solutions for large-scale problems through traditional optimization techniques. However, our focus lies in achieving comprehensive solutions that blend heuristics, mathematical optimization, like mixed-integer linear programming (MILP), and data analytics. This innovative hybrid approach can vastly outperform existing scheduling methods reliant on basic heuristics and rule-based approaches.

Going deeper into the solution space

In a batching context, the solution space represents the numerous ways to create batches with given WIP. Even in a small wafer fab with a basic batching toolset, this space is immense, making it impossible for a human to find the best solution in a multi-product environment. Batching policies throughout history have been like different paths for exploring this space, helping us navigate complex batching mathematics. Just as the Hubble space telescope aided space exploration in the 20th century, cloud computing and artificial intelligence now provide unprecedented capabilities for exploring the mathematical world of solution space, revealing possibilities beyond imagination.

With the advent of these cutting-edge technologies, it is now a matter of finding a solution that satisfies the diverse needs of a fab, including cost, lead time, delivery, quality, flexibility, safety, and sustainability. These objectives often conflict, and ultimately, finding the optimal trade-off is a business decision, but the rise of cloud and AI will enable engineers to pinpoint a batching policy that is closest to the desired optimal trade-off point. Mathematical optimization is an example of a technique that historically had hit its computational limitations and, therefore, its practical usefulness in wafer manufacturing. However, mathematicians knew there was a whole world to explore, just like astronomers always knew there were exciting things beyond our galaxy. Now, with mathematicians having their own big telescope, the wafer manufacturers are ready to set their new frontiers.

Ben Van Damme, Industrial Engineer and Business Consultant, Flexciton
Dennis Xenos, CTO and Cofounder, Flexciton


[1] FabTime Newsletter: Issue 24.03

[2] Wallace J. Hopp, Mark L. Spearman, Factory Physics: Third Edition. Waveland Press, 2011

[3] Lars Mönch,  John W. Fowler,  Scott J. Mason, 2013, Production Planning and Control for Semiconductor Wafer Fabrication Facilities, Modeling, Analysis, and Systems, Volume 52, Operations Research/Computer Science Interfaces Series 

[4] FabTime Newsletter: FabTime Cycle Time Tip of the Month #4: Use a Greedy Policy when Loading Batch Tools

[5] FabTime Newsletter: Issue 9.03 

[6] Sebastian Knopp, 2016, Complex Job-Shop Scheduling with Batching in Semiconductor Manufacturing, PhD thesis, l’École des Mines de Saint-Étienne 

[7] S. J. Mason , J. W. Fowler , W. M. Carlyle & D. C. Montgomery, 2007, Heuristics for minimizing total weighted tardiness in complex job shops, International Journal of Production Research, Vol. 43, No. 10, 15 May 2005, 1943–1963   

[8] S. Elaoud, R. Williamson, B. E. Sanli and D. Xenos, Multi-Objective Parallel Batch Scheduling In Wafer Fabs With Job Timelink Constraints, 2021 Winter Simulation Conference (WSC), 2021, pp. 1-11

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Maximising Wafer Fab Performance: Harnessing the Cloud's Competitive Edge

To cloud, or not cloud, that is the question. As other industries make the leap towards cloud technology, uptake with chipmakers continues to lag behind. In this article, Laurence explores the potential benefits of cloud adoption to equip Fab Managers with the motivation to reconsider the question.

To cloud, or not cloud, that is the question.

Some might consider the opening statement a tad flippant in borrowing Hamlet's famous soliloquy. Yet, the internal struggle our hero feels agonising over life and death holds a certain likeness to the challenges faced by Fab Managers today. Businesses live and die by their decisions to either embrace or disregard new innovations to gain a competitive edge and nowhere is this truer than in the rough and tumble world of semiconductor manufacturing; Fairchild, National Semiconductor and S3 are just a few of those who did not last. [1][2][3]

Semiconductor manufacturing has had a long history of innovating, tweaking, and tinkering,[4] so it’s somewhat surprising that the sentiment towards cloud uptake has been weaker in the semiconductor industry compared to the wider market[5]. This article aims to explore some of the potential benefits of cloud adoption to better equip Fab Managers with the motivation to take another look at the cloud question.

Recap: What are the different types of Cloud?

Cloud computing encompasses public, private, and hybrid models. The public cloud (think Azure, AWS, Google Cloud and so on) offers rental of computational services over the internet, while the private cloud replicates cloud functionality on-premises. However, private clouds require a significant upfront investment, ongoing maintenance costs and a skilled in-house IT team to manage and maintain the infrastructure, making it a less appealing option for smaller firms. Hybrid cloud blends on-site and cloud resources for flexible workloads, segregating the most sensitive workloads to on-premise environments for the greatest control; however, control does not necessarily mean security, which will be discussed in a later article! 

Understanding the benefits of cloud

1.      The Latest Tech

Embracing the latest cloud technology offers wafer fab facilities, not just organisations, a direct path to heightened capabilities in their manufacturing processes through the use of digital and smart manufacturing technologies. By harnessing advanced computational powers such as real-time analytics; optimization[6]; and machine learning defects detection[7], fabs can maximise all their fundamental KPIs, ultimately leading to better business outcomes. McKinsey estimates that, compared to other semiconductor activities, manufacturing has the most to gain from the AI revolution (Fig. 1), and a key technology enabling this is will be the vast computational power of the cloud.[8]

Fig. 1: McKinsey estimates that the AI revolution could reduce semiconductor manufacturing costs by around $38bn.

Case Study: The Latest Tech Driving Improvements in Fab KPIs

Seagate achieved a 9% increase in moves
by utilising Flexciton’s cloud native platform and cutting-edge autonomous scheduling.

2. Redundancy, Scaling, Recovery and Updates

It is true that some of these technologies can be provided on-premises; however, cloud computing, in general, reduces downtime through redundancy, automated scaling, and disaster recovery mechanisms, ensuring seamless operation even during hardware failures or unexpected traffic spikes. Some estimates suggest that downtime can cost firms an eye-watering $1 million to $5 million per hour, depending on their size and sector. [9] By leveraging the cloud, the cost of operating disaster recovery services has demonstrated potential cost savings of up to 85% when comparing public to private options. [10] It is easy to speculate that for wafer fab critical infrastructure, the cost of downtime could be significantly higher.

Furthermore, the number of wafers processed within a fab can cause computational traffic spikes during busy periods for some applications. On-premises deployments would need to account for this, even if the resource is not in use all the time, which can add to inefficiencies, while public cloud can elastically scale down, meaning you only pay for what you use. 

Lastly, on-premises systems without the ability to monitor and update remotely are often many versions behind, prioritising perceived stability but research has shown increasing the rate of software iteration increases stability and resilience rather than weakening it. [11] Without the convenience of remote updates, legacy systems can become entrenched, with employees on the shop floor being hesitant to embrace change due to the fear of disrupting critical infrastructure and the expenses associated with upgrading IT infrastructure. This sets in motion a self-reinforcing cycle where the expenses and associated risks of transitioning increase over time, ultimately resulting in significant productivity losses as users continue to rely on outdated technology from decades past.

3. Specialisation and Comparative Advantage

Stepping back from the fab and taking a holistic view of the semiconductor manufacturing organisation reveals compelling economic arguments, both on macro and micro scales, for embracing cloud.

Allowing cloud providers to specialise in cloud computing while wafer fab manufacturers focus solely on wafer fabrication benefits the latter by freeing them from the complexities of managing IT infrastructure. [12] This collaboration allows wafer fab manufacturers to allocate their resources towards core competencies, leading to increased operational efficiency and superior wafer production.

Simply put, fabs do not build the complex tools they need to make their products, such as photolithography equipment; they purchase and utilise them in ways others can’t to produce market leading products. Why should utilising the tools of the cloud be any different?

On a macro level, the argument of specialisation also applies through comparative advantage.[13] Different continents and countries have comparative advantages in certain fields, Asia has long been a world leader in all kinds of manufacturing due to its vast populations.[14] The United States, on the other hand, has a tertiary education system which is the envy of the world; institutions like Stanford and MIT are household names across the globe, and this has provided the high technical skills needed to be the home of the technology start up. Utilisation of cloud technology and other distributed systems allows firms to take the best of what both regions have to offer, high tech manufacturing facilities from Singapore to Taiwan with the latest technology from Silicon Valley or perhaps London. Through the cloud, Fab Managers and organisations can leverage a single advanced technology across multiple fabs within complex supply chains. This eliminates the need for costly and experienced teams to travel across the globe or manage multiple teams in various locations with varying skill sets, all while locating facilities and offices where the best talent is.

In brief, semiconductor firms' fate could rest on one pivotal decision: adoption of cloud. This choice carries the promise of leveraging cutting-edge technology, fortifying resilience, and reaping a multitude of advantages. Notably, by transitioning to cloud-native solutions, Fab Managers can usher their organisations into an era of unparalleled competitiveness, all while enjoying a range of substantial benefits. Among these benefits, for example, is cloud-native architecture like Flexciton’s, promising lower cost of ownership and zero-touch maintenance for fabs. We will delve deeper into the crucial aspect of security in one of our upcoming blogs, providing a comprehensive understanding of how cloud-native solutions are actually key to safeguarding sensitive data and intellectual property, rather than compromising it. In this era of constant innovation, embracing the cloud is more than just an option; it’s becoming a strategic imperative.

Author: Laurence Bigos, Product Manager at Flexciton


[1] Investor relations - Texas Instruments completes acquisition of National Semiconductor - Texas Instruments

[2] ON Semiconductor Successfully Completes Acquisition of Fairchild Semiconductor for $2.4 Billion in Cash

[3] S3 Graphics: Gone But Not Forgotten | TechSpot

[4] Miller, C. (2022). Chip War: The Fight for the World's Most Critical Technology. Scribner.

[5] Flexciton | Blog & News | Is Fear Holding Back The Chip Industry’s Future In The Cloud?

[6] Flexciton | Resources | Seagate Case Study 2.0

[7] Lynceus: Inline, Real-time, AI Based Process Control Monitoring That Can Reduce Inspection & Metrology Capex (semianalysis.com)

[8] Applying artificial intelligence at scale in semiconductor manufacturing | McKinsey

[9] Know Key Disaster Recovery Statistics And Save Your Business (invenioit.com)

[10] Wood.pdf (usenix.org)

[11] Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps. IT Revolution Press.

[12] Specialization Definition (investopedia.com)

[13] What Is Comparative Advantage? (investopedia.com)

[14] Why China Is "The World's Factory" (investopedia.com)