Is Fear Holding Back The Chip Industry’s Future In The Cloud?
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The semiconductor industry is at the cutting edge of technology – so why is it still so nervous about the cloud? Persisting with an outmoded security model means missing out on significant gains in manufacturing.
Only the paranoid survive?
Perhaps more than any other sector in the world, the semiconductor industry is incredibly protective of its intellectual property (IP). Given the centrality of the silicon chip to modern life, that’s not surprising – companies are in a constant arms race to design and develop ever more sophisticated chips to meet the never-ending demand for innovation from their customers. A design breakthrough could be worth billions of dollars, and so the security of the relevant data is paramount.
And that’s not the only threat that keeps semi co security teams awake at night – there’s the security of the actual chips themselves to consider. An ongoing fear within both the industry and among government security agencies is that rogue code may be inserted into a chip either during development or the manufacturing process, making any system it becomes part of vulnerable to attack.
In fact, security of manufacturing – with many companies now sub-contracting to facilities in Asia – has been explicitly cited as a key reason for building more fabs in the US. In March 2022, President Joe Biden said that semiconductors are “so critical to our national security… that we’re going to create rules to allow us to pay a little more for them if they’re made in America.” In other words, security fears are so intense that the industry is willing to put prices up just for the supposed reassurance of having chips that aren’t produced overseas.
Although Biden’s worries over the threats to national security are not cloud related, they feed into a culture of fear that has become embedded into the semiconductor industry, hindering its advancement towards next-gen technologies.
The cloud revolution
The cloud has revolutionised the way that business works in the 21st century in a number of ways. For a start, it’s decentralised the IT function – applications that would previously have resided in on-premise server rooms are now accessed as a service via the cloud. This has significantly simplified the set-up and running of satellite offices and local branches because there’s no need to house and manage IT hardware at every location – all that’s needed is a connection to the internet.
But for hi-tech companies, the real advantage of the cloud is the ability to access vast amounts of computing power on demand. Whether it’s for data crunching a massive set of figures, running an AI model through its paces, or simply trying to crack a really complex problem, the muscle provided by cloud computing can dramatically speed the process up.
On the face of it, this would make the semiconductor industry an obvious candidate for the widespread adoption of cloud technology. But that hasn’t been the case. Limited adoption has taken place – though usually relating to ‘non-critical’ business functions – but compared to the companies they serve, semi cos have been conspicuously slow to embrace the potential of the cloud.
Outmoded assumptions and intransigence
For an industry on the cutting edge of technological innovation, the reasoning behind this state of affairs seems to be based on outdated assumptions, an indication perhaps of just how embedded the fear culture is. The security philosophy at many chip makers is still predicated on each separate facility being a castle under siege that needs to be protected from external attack. The idea of willingly opening up these defences to the cloud is anathema.
Another factor holding back the full embrace of the cloud at chip companies and fabs is the fear of change. Many IT and security managers simply don’t recognise the new world of serverless functionality that the cloud can bring, and are quite happy to stick with the existing model. And there are IT teams that do understand the possibilities of cloud, but are frightened by what they imagine will be a massive upheaval of their working lives and environment, from having to create new security policies to potentially making themselves redundant. Without the pressure to change that has come from the top in other industries, IT itself is blocking cloud adoption.
Yet as both design and manufacturing processes become more complex, this reluctance to change isn’t tenable in the long-term. As chips become more and more sophisticated, the need to access computing power at scale will increase – and that means companies either building bigger server farms and private data centres, or properly embracing the cloud paradigm.
The fact is that cloud security has improved immeasurably over the past decade. According to a recent report from Accenture, “Today’s cloud solutions offer enhanced security and automation technologies that aren’t available for on-premise systems, making cloud a better option for preventing IP theft.” And refusing to move with the times because it threatens to disrupt the status quo is an increasingly questionable excuse from an industry built on pushing the technological envelope.
Ultimately, semiconductor companies have only fear and intransigence holding them back from total cloud adoption.
The end of on-premise production scheduling?
If the industry is to continue to innovate and keep up with the demands of its customers, it needs to produce highly sophisticated, next generation chips at scale. The only way to do that is by adopting smart manufacturing practices and technologies - and that means fully embracing the cloud. Why? Because current on-premises scheduling systems are no longer fit for purpose to handle the new levels of manufacturing complexity that next gen chips demand.
In an enclosed, siloed environment, such as exists in most current fabs, a typical on-premise scheduling system will only have access to so much computing power. Traditionally, these constraints have resulted in a reliance on heuristics to predict and control production workflow, as this is the best that can be achieved with the resources available. However, although these systems often use real-time data, the decisions they make are still based on rules that are created based on human experience from the past. The dynamic nature of a fab means that these rules are never going to stay pertinent, thus resulting in suboptimal production decisions.
By connecting the fab to the cloud, these power constraints disappear – and with them the restrictions that previously forced fabs to use heuristics-based scheduling. With access to a new magnitude of compute, companies can deploy more sophisticated systems able to schedule production based on real-time information, and thus optimize the manufacturing process.
Thanks to the power of the cloud, this next generation of scheduling systems is able to use complex mathematical algorithms to search through the billions of possible WIP permutations and make the best scheduling decision with present-time accuracy. This AI-based approach to scheduling requires a huge amount of computing power to rapidly work out the fab’s optimal position, but the cloud makes it possible to perform these calculations at unparalleled speed.
In theory, it is possible to get good computational power on-premise. The system would most likely be chosen based on what is cost-effective at the time and the power needed to solve the problem a fab had on that day. However, new computational power becomes more available and cost effective all the time. Moreover, fab complexity can easily change. For example, introducing a larger product mix into the fab could exponentially increase the complexity of the scheduling problem. With cloud, you can improve your hardware – and hence your KPIs – almost immediately. Something that is extremely unlikely on-premise due to the practical implications for the IT department.
And what could be a greater incentive to become cloud-friendly than fab capacity increases of up to 10%, which is what we’ve seen using these next gen systems? That’s the type of figure which should help even the most security-conscious chip company to change their mind about cloud technology.
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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: maximize batch sizes, minimize rework, and significantly reduce reliance on shop floor decision-making. The challenge was magnified by the fab's limited IT and IE resources at the time of deployment. Partnering with an existing vendor whose systems were already integrated and had immediate access to essential data facilitated a rapid and efficient implementation with minimal engagement of the fab's IT team. This deployment led to remarkable improvements: clean tools saw 25% bigger batches, and rework in the diffusion area was slashed by 36%.
Another case study details a full fab deployment, where the existing rules-based scheduling system was replaced with Flexciton's AI scheduler. The goal was to enhance capacity and reduce cycle times. The deployment was staged, beginning with simpler areas starting with metrology tools, through the photolithography area and eventually scaling to the entire fab, yielding a global optimization of work-in-process (WIP) flow. The result was a significant increase in throughput and a staggering 75% reduction in manual flow control transactions, a testament to the AI's ability to autonomously optimize WIP flow and streamline operations.
The Autonomous Future of Semiconductor Manufacturing
In closing, the semiconductor industry stands on the precipice of a new era marked by autonomy. AI technology, with its capacity to make informed decisions without human input, has demonstrated not only the potential for improved KPIs but also a significant reduction in the need for human decision-making. The future of semiconductor manufacturing is one where AI-driven solutions consistently deliver superior production results, alleviating the human workload and steering fabs towards their objectives with unprecedented precision and efficiency.
As we embrace this autonomous future, it becomes clear that the integration of AI in semiconductor manufacturing is not just an enhancement of the status quo but a reinvention of it. With each fab that turns to AI, the industry moves closer to realizing a vision where technology and human ingenuity converge to create a landscape of limitless potential.
Author: Jamie Potter, CEO and Cofounder, Flexciton
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.