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cloud technology cloud-native semiconductor industry wafer fabs cloud adoption security risk cost of cloud AWS azure public cloud software hack X-FAB hacked
 min read
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 (

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

[9] Know Key Disaster Recovery Statistics And Save Your Business (

[10] Wood.pdf (

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

[12] Specialization Definition (

[13] What Is Comparative Advantage? (

[14] Why China Is "The World's Factory" (

autonomous scheduling flexciton semiconductors industry tsmc samsung optimization fps production scheduling wafer fabs photolithography taxis new york
 min read
Autonomous Scheduling: A Tale of Three Taxis

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. Seb Steele offers an analogy to show how significant the difference is.

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

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

We are excited to introduce the Flexciton Tech Glossary Blog Series: A deep dive into the A-Z of semiconductor technology and innovation. In the first edition of the series, Ioannis Konstantelos and Dennis Xenos take a dive into AI and its applications in semiconductor manufacturing.

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,

[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

flexciton the flex factor culture hiring jobs vacancy vacancies optimisation optimization wafer fabs semiconductors semi industry
 min read
The Flex Factor with... Nitin

Meet Nitin, our Senior DevOps engineer and security guru. Keep reading to learn a bit more about him and what it's like work in DevOps at Flexciton.

Meet Nitin, our Senior DevOps engineer and security guru. Keep reading to learn a bit more about him and what it's like to work in DevOps at Flexciton.

Tell us what you do at Flexciton?

I’m a Senior DevOps engineer in the Platform Engineering Team (PET), which involves building highly available, scalable infrastructure to support various customers deployments, building infrastructure as code and supporting existing environments. I also have an important role to play as a Security Lead, to ensure Infrastructure and Application development follows standard security principals, dealing with Security incidents/threats and resolving them.

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

A typical day starts with looking at my slack channels to see if there are any issues with infrastructure or customer environments, and checking high priority security incidents raised by our systems. After that we have a scrum meeting to discuss how we are dealing with current tasks, any hurdles and get ready for another challenging day. If I have a dedicated security day then I would be looking at our security board in SecureFrame and begin fixing various failing security tests as we aim to soon get our SOC2 certificate. Also, looking at our SIEM dashboard, check security threats and build smart alerts to help us capture issues beforehand. If I am on DevOps tasks then I will be helping the PET team with building infrastructure for new customers, getting best practices set, and making sure that our development team can build infrastructure.

What do you enjoy most about your role?

What I enjoy most about my role at Flexciton is the variety of tasks and responsibilities that come with being a Senior DevOps and leading the Security work. As we are aiming for a SOC2 certification, I love the challenges I am facing, the various ways I need to consider before making infrastructure changes, and keeping myself always on top of security incidents to make sure I am not missing anything. 

From DevOps point of view, I really enjoyed working on the various initiatives/tech debts including logical grouping of infrastructure, and validation of Azure resources like why/when/how we will use them.

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

Grit, innovation, challenges. 

What career advice would you give to your younger self? 

“Never settle down”.

Keep pushing yourself.

If you could swap jobs with anyone for a day, who would they be and why?

I’d love to be an Astronaut. Watching our planet and enjoying the beautiful views of the galaxy. It wouldn't be easy to acquire the skills I'd need, but given a chance I'd give it a try!

Tell us about your best memory at Flexciton?

My favourite memory was when we recently celebrated the 7th anniversary of Flexciton on a boat. We floated around the Thames and enjoyed the lovely views across the river.  

flexciton culture hiring team teamwork semiconductors wafer fab smart manufacturing ai production scheduling optimization
 min read
The Flex Factor with... Sully

Meet Sully, the Bucket Brigade team's backend wizard, as he shines in the spotlight for July's edition of The Flex Factor. Discover more about the diverse challenges he tackles during his day-to-day and the valuable career advice he wishes he had known earlier.

Meet Sully, the Bucket Brigade team's backend wizard, as he shines in the spotlight for July's edition of The Flex Factor. Discover more about the diverse challenges he tackles during his day-to-day and the valuable career advice he wishes he had known earlier.

Tell us what you do at Flexciton?

I’m the Bucket Brigade team's backend developer, whereas the others are optimisation engineers, a frontend developer, a product owner, and some riff raff. Though it’s not a hard separation, this means I tend to do the backend work for our team that isn’t very optimisation focused.

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

Every day starts off with standup during which we are entertained by bad puns by our team scrum lord, Charlie, and acquire strange German words as sprint names from our German team member, Jannik. During stand up, I often say that I think I'll get something done that day, but then have to say I'm not done with it yet at least one additional morning. The rest of the day usually consists of working solo, doing some code reviews, and not going to too many meetings, unlike some colleagues who've made poor life choices and have frequent meetings. I usually leave between 6 and 7 PM, which is okay because I roll into the office pretty late every morning. I also make great use of the free snacks and colas. 

What do you enjoy most about your role?

I like that we have a variety of different kinds of problems to solve. It's engaging and there are many things we need to do and improve so there are always ways to find new challenges.

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

Live. Laugh. Optimize (with a z).

What career advice would you give to your younger self? 

Focus on skills that will follow you from role to role and across companies, rather than company or product-specific skills and knowledge.

If you could swap jobs with anyone for a day, who would they be and why?

I’d maybe swap with a fighter pilot. I feel like on the day I’m there I could improvise and manage to get up in the air, have fun, and figure out how the ejector seat works. Since I’m gone the next day I won’t have to deal with the consequences.

Tell us about your best memory at Flexciton?

I had a lot of fun in Albufeira. Though my memory is hazy, I’m pretty sure it was lots of fun. If I have that memory in my subconscious somewhere I bet it is good

semicon west semi semiconductor event conference labour shortage san francisco moscone chips act strategy usa
 min read
Come and Visit Our Booth at SEMICON West This July!

From 11–13 July 2023, Flexciton will be returning to San Francisco for this the latest edition of SEMICON West. And this time, we’ll be joining the Techworks / NMI members zone, where we will have our own stand – located at booth 945. 

From 11–13 July 2023, Flexciton will be returning to the cosmopolitan city of San Francisco for this the latest edition of SEMICON West. And this time, we’ll be joining the Techworks / NMI members zone, where we will have our own stand – located at booth 945. 

Drop by and visit our stand if you would like to meet with a member of our team to learn more about what autonomous scheduling could do for your fab. We can discuss how Flexciton can help you achieve your fab's unique objectives, how our scheduler can deal with the constraints you have and even conduct a live product demo session tailored to you. Or if you would prefer to just drop by for a warm drink and a chat, we'd be more than happy to have you!

The theme for this year’s event is “Building a Path Forward”, where a particular focus will be placed on the key challenges affecting the global microelectronics industry, including; Supply Chain Disruptions, Climate Change, and Talent Shortages. All of which need addressing to enable a $1T semiconductor industry.

SEMICON West provides a pathway for attendees to engage, learn, and conduct business under 3 key industry priorities that will be integrated into the Keynotes as a daily theme. We’re excited to be a part of the event once again. 

Key details:

11–13 July 2023
Flexciton located at booth 945
Event located at the MOSCONE Convention Center, San Francisco
Register for the event here:

Interested in learning more about what Flexciton can do for your fab? We’re hosting a free-to-join webinar with our partner, FabTime, on 27 June. Find out more and register by following this link: