Managing The Human Side Of Smart Manufacturing
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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Meet Lio, a driving force behind client success as Flexciton's Technical Customer Lead. Discover more about her keen eye for collaboration and passion for innovation in this edition of The Flex Factor.
Meet Lio, a driving force behind client success as Flexciton's Technical Customer Lead. Discover more about her keen eye for collaboration and passion for innovation in this edition of The Flex Factor.
Tell us what you do at Flexciton?
I’m a Technical Customer Lead.
What does a typical day look like for you at Flexciton?
The day is incredibly busy and passes quickly while collaborating with the customer team and other teams at Flexciton, making rapid progress day by day. My focus revolves around ongoing customer work, such as our work at Renesas (analyzing their adherence, checking the Flex Global heat map, and listening to feedback from the client). Additionally, I often work on live demos and PoC projects. The nature of my tasks varies depending on the project stage, ranging from initial data analysis and integration to final stages where I collaborate with sales on deliverables and the story of the final report. While consistently moving forward with projects and meeting weekly targets, we concurrently establish our working methods and standardize processes to improve efficiency for future projects. For lunch, I usually go to Atis, my go-to place for fresh and nutritious meals. People in the office call it a salad, but I consider it the best healthy lunch with the highest ROI.
What do you enjoy most about your role?
I find the most enjoyment in witnessing the impact our product has on customers who need it. It's fulfilling to see their reactions when they share challenges, and I appreciate understanding how Flexciton can collaborate with them, providing that extra element for improvement.
If you could summarize working at Flexciton in 3 words, what would they be?
Creative, Fast, Collaborative.
Given the fast-paced evolution of technology, what strategies do you recommend for continuous learning and skill development in the tech field?
Stay closely connected to the client side. Understanding the technology they're developing and their current tech level (MES and other systems) provides insights into their readiness for Flexciton.
In the world of technology and innovation, what emerging trend or development excites you the most, and how do you see it shaping our industry?
The semiconductor industry's rapid evolution and diversity are fascinating. The competition between TSMC and Samsung Foundry in advanced GAA (gate-all-around) technology is particularly intriguing. While Samsung claims to be ahead, industry voices suggest a bluff with poor yields. The competition is ongoing, and I wonder if TSMC will maintain its lead or if there will be a paradigm shift in the industry.
Tell us about your best memory at Flexciton?
Meeting the Renesas team at their fab in Palm Bay and witnessing one of their operators' reaction to our app was a memorable experience. Kodi, a talented young manufacturing specialist, was genuinely impacted by our technology which was amazing to see in person. After returning home, he even had a piece of code named after him by Amar.
Do you think you have what it takes to work at Flexciton? Visit our careers page to browse our current openings.
AI has unquestionably stood out as the prevailing technological theme of the year. This wave of innovation begs the question: how can the semiconductor industry, which stands at the heart of technological progress, leverage AI to navigate its own intricate challenges?
The dominant technological theme of the year is unmistakably clear: artificial intelligence (AI) is no longer a distant future, but a transformative present. From the startling capabilities of conversational ChatGPT to the seamless navigation of autonomous vehicles, AI is demonstrating an unprecedented ability to manage complexity and enhance decision-making processes. This wave of innovation begs the question: how can the semiconductor industry, which stands at the heart of technological progress, leverage AI to navigate its own intricate challenges?
Complexity-driven Challenges
Semiconductor wafer fabs are marvels of modern engineering, embodying a complexity that rivals any known man-made system. These intricate networks of toolsets and wafer pathways require precision and adaptability far beyond the conventional methods of management. The difficulty of this task is compounded by the current challenges that hinder its dynamic pace: a protracted shortage of skilled labor, technological advancement in product designs, and the ever-present volatility of the supply chain.
The latest generation of products is the pinnacle of complexity, with production processes that involve thousands of steps and incredibly intricate constraints. This complexity is not just a byproduct of design; it is an inherent challenge in scaling up production while keeping costs within reasonable limits.
The semiconductor supply chain is equally complicated and often susceptible to disruptions that are becoming all too common. In this context, the requirement for skilled labor is more pronounced than ever. Running fab operations effectively demands a workforce that's not just technically skilled but also capable of innovative thinking to solve problems of competing objectives, improve processes, and extract more value. No small task in an environment already brimming with complexity.
The Need for AI in Semiconductor Manufacturing
As we delve into Industry 4.0, we find ourselves at a crossroads. The software solutions of today, while advanced, are not the panacea we once hoped for. The status quo has simply reshuffled the problems we face; we've transitioned from relying on shop floor veterans' tacit knowledge and intuition to a dependency on people who oversee and maintain the data in digital systems. These experts manning the screens are armed with MES, reporting, and legacy scheduling software, all purporting to streamline operations. Yet, the core issue remains: these systems still hinge on human intelligence to steer the intricate workings of the fabs.
At the core of these challenges lies a common denominator: the need for smarter, more efficient, and autonomous systems that can keep pace with the industry's rapid evolution. This is precisely where AI enters the frame, poised to address the shortcomings of current Industry 4.0 implementations. AI is not just an upgrade—it's a paradigm shift. It has the capability to assimilate the nuanced knowledge of experienced engineers and operators working in a fab and translate it into sophisticated, data-driven decisions. By integrating AI, we aim to break the cycle of displacement and truly solve the complex problems inherent in wafer fabs management. The potential of AI is vast, ready to ignite a revolution in efficiency and strategy that could reshape the very fabric of manufacturing.
Building AI for the Semiconductor Industry
Flexciton is the first company that built an AI-driven scheduling solution on the back of many years of scientific research and successfully implemented it into the semiconductor production environment. So how did we do it?
Accessing the Data
The foundation lies in data – clean, accessible, and comprehensive data. Much like the skilled engineers who intuitively navigate the fab's labyrinth, AI requires a map – a dataset that captures the myriad variables and unpredictable nature of semiconductor manufacturing.
Despite the availability of necessary data within fabs, it often remains locked in silos or relegated to external data warehouses, making it difficult to access. Yet, partnerships with existing vendors can unlock these valuable data reserves for AI applications.
Finding People Who Can Build AI
The chips that enable AI are designed and produced by the semiconductor industry, but the AI-driven applications are developed by people who are not typically found within the sector. They align with powerhouses like Google and Amazon or deep-tech companies working on future-proof technologies. This reveals a broader trend: the allure of semiconductors has diminished for the emerging STEM talent pool, overshadowed by the glow of places where state-of-the-art tech is being built. Embracing this drift, Flexciton planted its roots in London, a nexus of technological evolution akin to Silicon Valley. This strategic choice has enabled us to assemble a diverse and exceptional team of optimization and software engineers representing 22 nationalities among just 43 members. It's a testament to our commitment to recruiting premier global talent to lead the charge in tech development, aiming to revolutionize semiconductor manufacturing.
AI Needs Cloud
The advent of cloud computing marks a significant milestone in technological evolution, enabling the development and democratization of technology based on artificial intelligence. At the core of AI development lies the need for vast computing power and extensive data storage capabilities. The cloud environment offers the ability to rapidly provision resources at a relatively low cost. With just a few clicks, a new server can be initialized, bypassing the traditional complexities of hardware installation and maintenance typically handled by IT personnel.
Furthermore, the inherent scalability of the cloud means that not only can we meet our current computing needs but we can also seamlessly expand our resources as new technologies emerge. This flexibility provides collaborating fabs with the latest technology while avoiding the pitfalls of significant initial investment in equipment that requires regular maintenance and eventually becomes obsolete.
Security within the cloud is an area where misconceptions abound. As a cloud-first company, we often address queries about data security. It's crucial to understand that being cloud-first does not equate to possessing your data. In fact, your data is securely stored in Microsoft Azure data centers, which are bastions of security. Microsoft's commitment to cyber security is reflected in its employment of more than 3,500 professionals whose job is to ensure that data centers are robust and a fortress for data, offering peace of mind that often surpasses the security capabilities of private data centers.
Effective Deployment of AI in Fabs
The introduction of AI-driven solutions within a fab environment entails a significant change in existing processes and workflows and often results in decision-making that diverges from the traditional. This can unsettle teams and requires a comprehensive change management strategy. Therefore the implementation process must be planned as a multifaceted endeavor and deeply rooted in human collaboration.
A successful deployment begins with assembling the right team—a blend of industrial engineers with intimate knowledge of fab operations, and technology specialists who underpin the AI infrastructure. This collective must not only include fab management and engineers but also those who are the lifeblood of the shop floor—individuals who intimately understand the fab's heartbeat.
When it comes to actual deployment, the process is iterative and data-centric. Setting clear objectives is pivotal. The AI must be attuned to the Fab's goals—be it enhancing throughput or minimizing cycle times. Often, the first output may not align with operational realities—a clear indication of the AI adage that the quality of input data dictates the quality of output. It is at this juncture that the expertise of Fab professionals becomes crucial, scrutinizing and correcting the data, and refining the schedules until they align with practical Fab dynamics. With objectives in place and a live scheduler operational, the system undergoes rigorous in-FAB testing.
Change management is the lynchpin in this transformative phase. The core of successful AI adoption is rooted in the project team's ability to communicate the 'why' and 'how'—to educate, validate, and elucidate the benefits of AI decisions that, while novel, better align with overarching business goals and drive performance metrics forward.
Making AI Understandable and Manageable
The aversion to the enigmatic 'black box' is universal. In the world of fabs, it can be a barrier to trust and adoption —operational teams must feel empowered to both grasp and guide the underlying mechanisms of AI models.
We made a considerable effort to refine our AI scheduler by incorporating a feature that enables the user to influence the objective of what our AI scheduler is tasked to achieve and also to understand the decision. Once a schedule is created, engineers can look through those decisions and inspect and interrogate them to understand why the scheduler made these decisions.
Case Studies: Success Stories of AI Deployment
I firmly believe that we are on the cusp of a transformative era in semiconductor manufacturing, one where AI-driven solutions will yield unprecedented benefits. To illustrate this, let's delve into some practical case studies.
The first involves implementing Flexciton's AI scheduler within the complex diffusion area of a wafer fab—a zone notorious for its intricate processes. We aimed to achieve a trifecta of goals: reducing batch sizes, minimizing rework, and significantly reducing reliance on shop floor decision-making. The challenge was magnified by the fab's limited IT and IE resources at the time of deployment. Partnering with an existing vendor whose systems were already integrated and had immediate access to essential data facilitated a rapid and efficient implementation with minimal engagement of the fab's IT team. This deployment led to remarkable improvements: clean tools saw 25% bigger batches, and rework in the diffusion area was slashed by 36%.
Another case study details a full fab deployment, where the existing rules-based scheduling system was replaced with Flexciton's AI scheduler. The goal was to enhance capacity and reduce cycle times. The deployment was staged, beginning with simpler areas starting with metrology tools, through the photolithography area and eventually scaling to the entire fab, yielding a global optimization of work-in-process (WIP) flow. The result was a significant increase in throughput and a staggering 75% reduction in manual flow control transactions, a testament to the AI's ability to autonomously optimize WIP flow and streamline operations.
The Autonomous Future of Semiconductor Manufacturing
In closing, the semiconductor industry stands on the precipice of a new era marked by autonomy. AI technology, with its capacity to make informed decisions without human input, has demonstrated not only the potential for improved KPIs but also a significant reduction in the need for human decision-making. The future of semiconductor manufacturing is one where AI-driven solutions consistently deliver superior production results, alleviating the human workload and steering fabs towards their objectives with unprecedented precision and efficiency.
As we embrace this autonomous future, it becomes clear that the integration of AI in semiconductor manufacturing is not just an enhancement of the status quo but a reinvention of it. With each fab that turns to AI, the industry moves closer to realizing a vision where technology and human ingenuity converge to create a landscape of limitless potential.
Author: Jamie Potter, CEO and Cofounder, Flexciton
Introducing Will, Lead Backend Engineer at Flexciton. Explore his daily tasks, ranging from crafting backend architecture to overseeing the codebase and managing technical debt in this month's edition of The Flex Factor.
Introducing Will, Lead Backend Engineer at Flexciton. Explore his daily tasks, ranging from crafting backend architecture to overseeing the codebase and managing technical debt in this month's edition of The Flex Factor.
Tell us what you do at Flexciton?
I am a lead backend engineer and the software development practice lead. My work involves designing the backend architecture, managing the codebase structure and technical debt, pushing for best practices across the wider engineering team and contributing features to my delivery team.
What does a typical day look like for you at Flexciton?
I usually start my morning by scanning through the production logs from our deployments and seeing if anything looks suspect and in need of an investigation. From there it will depend on what I am focused on for that week which tends to vary a fair amount. The majority of my time is spent coding features or doing large scale design work. Some days I get to spend refactoring and restructuring our codebase, occasionally I will get to work in the devops or optimisation space which I always look forward to. In any given week there will be a handful of ongoing projects at various stages, from architectural designs to software development practice work that needs to be structured and prioritised. No day goes by without me writing at least some code, but there is a fair amount of admin work to do as well.
What do you enjoy most about your role?
The diversity of the work I get to do. My work often overlaps with optimisation and devops so I can find myself speaking the lots of different people throughout the day. There are many opportunities to dive deeper into a topic with various team members willing to support you. Since joining I have worked with terraform, CI pipelines, infrastructure, hardware configuration, optimisation, frontend, customer deployments, database optimisation and management, the application backend and much more.
If you could summarise working at Flexciton in 3 words, what would they be?
Collaborative, Challenging, Diverse.
What emerging technology do you believe will have the biggest impact on our lives in the next decade?
I think the next decade is going to be made great by lots of smaller contributions made across technology from both hardware and software. I don’t have much hope for AGI / useful AGI this decade but there is a lot going on to be excited about. From a hardware perspective we have companies making huge progress in designing chips specifically for model training, and at the other end of the spectrum more companies are putting satellites into orbit to enable global access to high speed internet. AI has fuelled the search in identifying stable structures for proteins and crystals, pushing frontiers of new medicines and treatments, as well as material science. Memory safety in programming languages has started to draw attention from governments too with languages like Rust (and potentially Hylo in the future) likely to lead for memory safe applications. It will be interesting to see how the landscape changes over the next few years and see companies start to shift their codebases over.
What’s the best piece of advice you’d give to someone starting a career in the tech industry today?
I think the best piece of advice would be to throw away any notion of imposter syndrome from the start. Programming, and tech in general, is massive, and its certainly true that the more you know, the more you realise you do not know. Everyone will take a different path throughout their career and find themselves being expert in one topic and (momentarily) hopeless in another. When the topics that you know nothing about come along, its best to embrace that and start finding opportunities to learn. It is important to convince yourself that while you may not be able to learn everything, you could learn anything and find joy in accruing that knowledge as you progress in your career. Bearing this in mind, I would say come into tech because you love it and because you want to learn. There is such as good community across programming languages and industries, anyone who wants to learn can easily find help.
Tell us about your best memory at Flexciton?
I can’t think of one great memory that stands out, but what makes Flexciton great is all the little things that happen week after week such that by Sunday evening, I am looking forward to speaking with my team in Monday standup.