In this month’s edition of The Flex Factor, we introduce one of our QA Engineers: Yichen Tian. Have a read to find out what this serial multitasker does during her day-to-day.

Tell us what you do at Flexciton?

I am a QA engineer in the Chrysalis team, which involves checking if the result of our development makes sense with automated and manual tests. I’ve also recently joined our Platform Engineering Team, whose mission is to make other developer’s lives easier by building faster CI/CD pipelines and laying the ground work of our architecture.

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

My day starts with a coffee and then a short gathering with all the team members to share updates and prepare for the day. During the day I discuss with developers about the appropriate outcomes of different user scenarios and meddle with services like GitLab and Kubernetes for the rest of my day.

What do you enjoy most about your role?

I feel it’s the excitement. As a QA I constantly switch contexts and have more than three tasks simultaneously on a busy day. I also troubleshoot pipelines and any breakage in our app and that constant change excites me.

What career advice would you give to your younger self?

To quote a member of the team, just keep swimming.

If you could do it all over again, would you pursue the same career?

I know it sounds like a cliche but I don’t regret the decisions I have made along the way. However I would love to work in an animal shelter for some time if the opportunity arises.

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

Inspiring, supportive, fun.

Tell us about your best memory at Flexciton?

There are so many amazing memories I have at Flexciton! Most of them are from team trips and day to day banter. One of the best has to be swimming in the sea together and watching my colleagues play beach football in Portugal.

As next-gen designs become increasingly sophisticated, a more holistic and streamlined approach to the manufacturing process is vital.

Why Has Sophistication Become An Issue?

As I’ve talked about in previous blogs, the semiconductor industry faces serious challenges on a number of fronts. 

The supply chain issues caused by Covid are still a headache. While some industries (automotive in particular) are putting pressure on chip companies to ramp up production, others, such as data storage, suffer from demand downturns. Another key factor impacting chip making is an ongoing shortage of skilled labour within the industry. Then there’s the problem of manufacturing equipment, with companies either unable to source second-hand tools or new tools being too expensive due to inflation. And as the world’s energy crisis continues, power itself – and skyrocketing electricity bills – is also a major concern.

‍As I discussed in my presentation at last year’s Fab Management Forum, the big issue that underlies all of these challenges is complexity. In many ways, fabs and the way they operate haven’t changed much in the past decade – yet the products they make have become increasingly sophisticated and as a result, more difficult to manufacture at scale. It’s not unusual now to see chip designs going into production with over 1,600 unique steps required to produce them, in cycle times that can stretch up to nine months. And as an example of just how complex chips are becoming, Micron recently began volume production of the world’s first 232-layer NAND.

This level of sophistication is only going to increase in the coming years, and the complexity challenge will soon reach breaking point if fabs continue with current practices. Unless fabs introduce new methods to streamline and simplify the management of the production process, their performance and output will continue to suffer, hindered by the sophistication of their own products.

What’s the problem with how fabs attempt to deal with complexity? Currently, they follow the classic model of addressing a big problem by breaking it down into a series of smaller, more manageable problems, with different teams assigned specific challenges to tackle. However, this approach has created problems of its own – different teams within the fab also have different priorities and KPIs, which they often work towards in isolation. And as individual teams try to max their KPIs, conflicts can arise that negatively impact production itself.

Production Scheduling Is Compromised By Simplification

Let’s drill down into the complexity issue and look at how it affects production scheduling in particular. 

There are a number of different areas within chip production – metrology, photolithography, diffusion furnace, epitaxy etc – which each have their own set of tools and rules as to how they operate. Each area also has its own team with their own KPIs. So while the overarching objective of a fab is to produce a required number of saleable wafers, each team also has more granular objectives against which they’re being measured.

Typically, teams schedule production in their areas according to a series of rules that dictate the sequence in which wafers are processed - for example, this particular recipe should always run on this particular tool. That sounds simple enough, except there can be thousands of these rules for each area – in fact, it’s so difficult for industrial engineers to properly manage and control each area’s parameters that the rules tend to be full of simplifications and shortcuts.

To maintain the fab’s performance, these rules also require regular maintenance to respond to different events happening in the fab on a daily basis. Yet given their sheer volume, and the growing complexity of the products being made, it’s impossible for teams to adapt every rule to address the real-time situation. An additional issue is that each area has its own software to administer these rules and monitor its KPIs, but it generally doesn’t interoperate with the software in other areas. 

All of which means that the teams aren’t able to see the status of each other’s area – they can only operate based on their own data. Not only have the rules they use have been simplified in an attempt to deal with complexity, but they’re designed to meet each area’s objectives, not the overarching goal of production. So while individual teams may be hitting their own KPIs, the overall performance of the fab is inconsistent. 

There is no ‘big picture’ of the production process that individual teams are able to consult to guide their decision-making – and as it is, they are not being judged on overall performance, just how well their own area is doing. But this is simply not a viable way for fabs to work going forward.

Embracing Complexity 

So what is the solution for handling production complexity on its own terms rather than constantly diluting it? It’s counter-productive to try and simplify data when it’s that very complexity that makes it so powerful – and by genuinely engaging with every aspect of it, it’s possible to gain a more accurate and comprehensive picture of what’s happening in the fab. Rather than simplifying the data, we should instead be simplifying the process.

The first step to managing complexity is employing an intelligent scheduling system that operates based on a holistic overview of what’s actually happening in the fab at any one time, identifying and responding to bottlenecks in the WIP as they happen. It also needs to make these adjustments and deliver schedules autonomously, because as we’ve seen, the complexity and unpredictability of modern fab operations make it infeasible for conventional rules-based schedulers to deliver consistent results. The constant requirement for manual retuning is a drain on IE resources, and the intelligence in the software itself is not advanced enough to effectively tackle the hardest problems found in a wafer fab. 

Is such an autonomous approach to scheduling possible? Short answer, yes it is, but it requires a willingness on the part of the semiconductor industry to a) fully embrace smart manufacturing practices, and b) to switch from their conventional scheduler to deploy a best-in-class technology that leverages both the power of the cloud and the computational speed of AI.

The complexity of modern chip design demands a new approach to production that is equal to this complexity – otherwise, the industry will be forever on the back foot, constantly struggling to keep up with the future while failing to capitalise on the richness of the data available to it in the here and now.
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Author: Jamie Potter, CEO and Cofounder of Flexciton

Meet James Adamson, one of our senior optimization engineers here at Flexciton. Many, many moons ago he was an aspirant farmer, now he’s designing and improving our scheduling algorithms. 

Tell us what you do at Flexciton?

I’m an Optimization Engineer, which essentially means I focus on designing and improving our scheduling algorithms, while also implementing and maintaining them in production code. I also have a technical lead role for one of our customers, so I spend some time understanding their requirements in detail and thinking about how to expand the product or customise it to meet their individual needs.  

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

In my engineering team we kick things off with a stand-up to agree on priorities for the day and discuss any issues that need attention. My day would then typically be a mix of drinking coffee, getting stuck into writing code for some new functionality, and having design discussions with other members of the team to keep us aligned technically. 

What do you enjoy most about your role?

I would say the opportunity to combine two things: working on one of the most challenging optimisation problems out there; and the ability to actually have an impact, for example through getting my code into production or making and influencing key design decisions.

If you could give one piece of advice to someone, what would it be? 

I would maybe suggest they seek advice from better places… but no, I think it’s important to always be thinking about what it is you want, and to think several steps ahead. It’s all too easy to get stuck doing something you don’t enjoy.

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

Interesting, challenging, impactful.

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

I used to want to be a farmer… so provided I could pick a day with decent weather then sure why not give that a go for day. I reckon it’s much harder work than the idea I used to have of chilling on a combine harvester though…

Tell us about your best memory at Flexciton?

There’s a whole bunch of memories from our team trips, most recently to Albufeira in Portugal where some people really shone with their dance moves. I will avoid naming names.

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Introduction

The first integrated circuits were invented by Texas Instruments and Fairchild Semiconductor in 1959. Today, semiconductor manufacturing is a $600 billion dollar industry and microchips are ubiquitous and impact our lives in ever increasing ways. To achieve such astonishing growth, academics and industry have had to constantly innovate, researching new production technologies. While much has been said about Moore's law and the push towards higher and higher transistor densities, the innovations made in how the billion dollar factories producing these chips are run have received less attention. This article focuses on innovations in scheduling: algorithms which assign lots to machines, decide in which order they should run, and ensure any required secondary resources (e.g. reticles) are available. These decisions can significantly impact the throughput and efficiency of wafer fabs.

Many innovative technologies in scheduling were first proposed by researchers and have, over time, been adapted in manufacturing. They include:

• Dispatching: rule-based systems for deciding which lot to run next on a tool 
• Optimization-based scheduling: mathematical techniques like mixed integer programming and constraint programming which can generate optimal machine assignments, sequencing, and more for entire toolsets or areas of the fab, improving fab-wide objectives like cycle-time or cost
• Simulation: computer models of the manufacturing process which are often used to run what-if analysis, evaluate performance, and aid decision making

From dispatching to mathematical programming

Early academic research on dispatching rules dates back to the 1980s. Authors at the time already highlighted the significant impact scheduling can have on semiconductor manufacturing. They experimented with different types of dispatching rules, ranging from simple first-in-first-out (FIFO) rules to more bespoke rules focused on particular bottleneck tools. Over time, dispatching rules have evolved from fairly simple to increasingly complex. Rule-based dispatching systems quickly became the state-of-the-art in the industry and continue to be popular for several reasons: they can be intuitive and easy to implement, yet allow covering varying requirements. There are, however, also many situations in which dispatching rules may perform poorly: they have no foresight and generally look only at a single tool and therefore often struggle with load balancing between tools. They also struggle with more advanced constraints such as time constraints or auxiliary resources, e.g. reticles in photolithography. More generally, dispatching systems are a mature technology that has been pushed to its limits and is unlikely to lead to significant increases in productivity and yields.

For these reasons, focus has shifted over time to alternative technologies, especially deterministic scheduling based on mixed-integer programming or constraint programming. In the academic literature, these approaches start to increasingly show up around the 1990s. Early contributions focused on analysing the complexity of the wafer fab scheduling problem and solved the resulting optimization problem using heuristic techniques, but slowly moved towards rigorously scheduling single machines, tackling one particular aspect of the problem at a time. Due to the limited scope deterministic techniques could initially tackle, their adoption in industry lagged behind the academic discussion. 

From single machines to fab-wide scheduling

The last twenty years have seen deterministic scheduling techniques mature and schedule larger and more complex fab areas. In the academic literature, authors moved from focusing on single (batching) tools, to entire toolsets or larger areas of the fab including re-entrant flows. They also started including more and more operational constraints such as sequence-dependent setup and processing times, time constraints, or secondary resources such as reticles. In order to achieve this increase in scale and complexity, researchers have applied a large number of optimization techniques, and often combined rigorous mathematical programming methods with heuristic approaches.  Some have used general purpose meta-heuristics, such as genetic algorithms or simulated annealing, while others have developed bespoke heuristics for fab scheduling, such as the shifting bottleneck heuristic. 

As the size of problems optimization-based scheduling techniques could solve grew, the industry started to explore how to adopt these methods in practice. For example, in 2006, IBM announced that it had successfully used a combination of mixed-integer programming and constraint programming to schedule an area of a fab with up to 500 lot-steps and that this had led to a significant reduction in cycle time. Our own technology at Flexciton leverages mathematical optimization and smart decomposition, combined with modern cloud computing, to efficiently schedule entire fabs. One key advantage of using cloud technology is the ability to access huge amounts of computational power. It allows to break down complicated problems and deliver accurate schedules every few minutes, as well as the ability to adapt the solution strategy to the complexity at hand. Additionally, it enables responsive adjustments, as events unravel in real-time, allowing for a truly dynamic approach to scheduling.

Optimization-based scheduling’s trajectory from an academic niche to a high-impact technology has partially been accelerated by two major trends:

• The increasing automation of wafer fabs and availability of process data. Especially automated material handling systems in modern fabs have made scheduling techniques almost a necessity. 
• The success of the semiconductor industry itself. Faster microprocessors have made it possible to solve bigger scheduling problems in less time. 

The process has been accompanied by considerable improvements in productivity, as scheduling is able to overcome many of the downsides of dispatching: it can look ahead in time, balance WIP across tools, and improve fab-wide objectives such as cost or cycle-time. A major advantage of scheduling is that it can both increase yields when demand is high and reduce cost when demand is low. 

When in doubt, simulate.

A discussion of scheduling in wafer fabs would not be complete without a word on simulation models. Simulation models are technically not scheduling algorithms - they require dispatching rules or deterministic scheduling inside them to decide machine assignment and sequencing. But they have been used to evaluate and compare different scheduling approaches from the very beginning. They were also quickly adopted by industry and have, for example, been used by STMicroelectronics to re-prioritise lots and by Infineon to help identify better dispatching rules. The development of highly reliable simulation models could greatly increase their use for performance evaluation and scheduling.

The future

More reliable simulation models are also important in light of recent trends in academic literature, which may provide a glimpse into the future of wafer fab scheduling. Rigid dispatching rules that need to be (re)tuned frequently may soon be replaced by deep reinforcement learning agents which learn dispatching rules that improve overall fab objectives. In some studies, such systems have been shown to perform as well as dispatching systems based on expert knowledge. If and when the industry adopts such techniques on a large scale remains to be seen. Since they require accurate simulation models as training environments, they can be extremely computationally intensive, and their adoption will largely depend on the development of faster training and simulation models. The combination of self-learning dispatching systems, and comprehensive, scalable scheduling models may well hold the key to unlocking unprecedented improvements in fab productivity. 

Flexciton aspires to be the key enabler in this transition, bringing state-of-the-art scheduling technology to the shop floor in a modern, sophisticated, and user-friendly platform unlike anything else on the market. Despite the enormous challenges that come with the scale of this endeavour, the initial results are very encouraging; cloud-based optimization solutions can indeed bring a step change to streamlining wafer fab scheduling while delivering consistent efficiency gains. 

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This month on The Flex Factor, we get to know our Senior People & Talent Partner, Charlotte Conway! Find out a little more about her and how she creates a supportive environment that helps our whole team to thrive.

Tell us what you do at Flexciton?

I work across both the People and Talent function as a Senior People & Talent Partner. I help Flexciton to find, attract and recruit top talent, and am responsible for engaging, supporting and developing our employees.

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

There is no such thing as a typical day in a startup! However, my day is often split 80% on the people side and 20% on talent. I like to start my day with any admin tasks or reply to any slack messages that might have come through. I then create a to-do list for what I plan to do that day. This can be dealing with employee queries, or business partnering with managers to check in on any people related matters. During busier periods I will often be taking a hands-on approach to hiring, sourcing and speaking to candidates as well as setting up our talent processes and looking at our employer branding strategy to help us to attract the best talent. As a startup there are also lots of projects to get involved in across all of HR (e.g. performance management, L&D) so a lot of my day may involve working on improving our people and talent processes... or implementing new processes!  

What do you enjoy most about your role?

What I enjoy most about my role is getting to work closely with our people (I guess it’s in the name, ‘people partner', right?). For me, the important part of being a ‘people’ partner is creating an environment where people feel heard, supported, and empowered to bring their best selves to work. Being able to have a small part in ensuring employees have all of the above is incredibly rewarding and fulfilling.

What's a quote that you live by?

“I've learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

― Maya Angelou

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

Exciting, dynamic and FUN.

If you could give one piece of career advice to someone, what would it be?

Never doubt yourself or let fear of failure hold you back. It’s ok to make mistakes and take risks! It’s better to look back and never have that feeling of ‘what if’ because you were too scared to take the next step.

Tell us about your best memory at Flexciton?

There are lots! However, It’s one of the many fun Flexciton socials that comes to mind - Dabbers Bingo. What better way to celebrate with your colleagues than with some good, old fashioned competition. There was dancing, music and of course bingo. This was then followed by a late night showing of Shrek in the office, and a very patient colleague (thanks Jannik) failing miserably to teach me how to ride a bike…I blame the one too many glasses of prosecco!

Interested in working at Flexciton? Head over to our careers page to to check what vacancies we currently have available and learn a little more about us whilst you're there.

Change management is just as important as new technology in a successful implementation.

People have the power

The core belief that drives the work we do here at Flexciton is that, for the semiconductor industry to advance to the next level of efficiency, it has to comprehensively embrace smart manufacturing practices.

As I’ve written previously, the rollout of smart manufacturing will require fabs to adopt ‘disruptive’ cloud-based, AI-driven technologies. As such, the move to smart manufacturing will be an absolute step change for most companies and will result in some fundamental adjustments to the way that the fab works. Yet ensuring that these new technologies integrate seamlessly with the existing systems is only part of the challenge.

Ultimately, the success of a smart manufacturing implementation will be decided by the people who work in the fab. For these new technologies to deliver the efficiencies they promise, there has to be total buy-in from the staff who are expected to work with them, particularly in legacy fabs where final decisions are still often made by humans. This is what we call the human side of smart manufacturing, and getting it right is just as important as deploying the technology itself.

What exactly is the human side? It’s the recognition that, for all the advanced tools and machinery, and the software that runs them, most fabs still depend on skilled workers to not only move the WIP around the factory floor, but also make decisions that are integral to the manufacturing process itself. As such, these workers are deeply invested in how the fab runs and take pride in the job they do.

Sensitive change management is critical

For implementation of smart technology to succeed, the human side of the transition has to be managed with skill, sensitivity and awareness. It’s not easy to shift existing work practices in any industry, and this is particularly the case within the semiconductor sector, which is used to doing things its own way.

Cutting-edge disruptive technologies are more often than not born in the minds of academics who, despite offering an innovative solution, may have a limited understanding of the inner workings of a fab. As a result, they can fail to take into account the complex implications of their technology and how the adoption phase can impact the people working with it. This is one of the reasons why fabs tend to stick with implementing conventional or in-house solutions. Despite being far less sophisticated, these technologies are built with an appreciation of the realities of a factory and the people who work there. Subsequently, the adoption process is smoother and, although it may not provide radical performance improvements, the impact can be more positive for the fab and its workers.

The key point here then is that, in order for a disruptive smart technology to be implemented successfully, it is critical to have a comprehensive understanding of a fab and a sensitive approach to human change management. Resistance to change is only natural and is to be expected, which is why from the word go, we work side-by-side with our clients to ensure that everybody is on board with changes to the way the fab works, because new technology on its own is not a silver bullet. Fabs don’t conform to theoretical models, but are subject to all manner of real world influences, with the human factor being especially strong.

The Flexciton approach

There are specific steps that we take to make the change management process as successful as possible:

Understand their challenges

To support the change management process, we build close relationships with our clients – not only to create trust, but also to develop a deep understanding of how their current processes work and how production has been executed so far. We have to identify both the objectives and ambitions of the management and the specific challenges and pain points that operators are facing on the shop floor. We do this by shadowing the roles of everyone at the fab who will be affected by the new implementation.

Educate and explain

Change management must follow a systematic methodology, but every fab is different, and so there’s no such thing as a standardised rollout. We understand that our technology will change the way in which both operators and engineers work, which is why the onus is on us to educate and explain why these changes are necessary – as already noted, unless something has gone disastrously wrong, people tend to be resistant to change, particularly if they think they are already doing a perfectly good job. To aid this transition, we always strive to give as much context to the decision-making process as possible.

Establish champions

As such, it is vital that we also establish client champions of the new solution who are already trusted by the fab’s staff, and can help navigate the acceptance process. This is particularly important if, for instance, the decisions that our advanced technology is suggesting initially seem counter-intuitive to those who are familiar and comfortable with old procedures.

Act on feedback

And just as important as demonstrating the results our technology is delivering in a way that’s easy to understand, we also regularly capture user feedback during the rollout period to see where our product and the user experience can be improved. Implementation is a constant process of testing and tweaking to produce the best possible results, and that requires an honest, two-way relationship to be in place. We regularly put new features into our product based on feedback from the shop floor, and it’s always satisfying to hear how we’ve improved operators’ ability to do their jobs as a result.

To undertake a successful smart manufacturing implementation, particularly as a third party vendor, it’s not enough to just have an innovative technology solution. To be a genuine change maker, you need to understand that in the real world, it’s the people that matter as much as the technology. This is why we always set out to build a strong partnership with the fabs that we work with, becoming much more than just an external vendor. Our team is committed to delivering on the KPIs that are targeted by our clients, which doesn’t stop at providing the best possible solution. We also have to understand the people who use it and ensure their adoption of our technology is a smooth and positive process.

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