The semiconductor industry needs to become more sustainable in a world of increasing demand – optimization holds the key.
Over the past decade, industries worldwide have had to tackle the issue of sustainability as a matter of increasing urgency, with the need to mitigate greenhouse gas emissions becoming a major factor in companies’ operations and processes. For many, this has been a difficult accommodation to make, because it has forced them to change the way they work. On the other hand, addressing the climate crisis has acted as a catalyst for the transition to more innovative and efficient methods of production and manufacture.
The semiconductor industry is at the heart of our modern technological society, and as recent supply chain issues have demonstrated, any slowdown in production has a significant knock-on effect on multiple other sectors. Given its centrality, and the need to maintain output, sustainability hasn’t always been as high on the agenda of chip companies as it should have been. However, ignoring the serious environmental impact that semiconductor industry has on the world around it is no longer tenable.
According to McKinsey, “large semiconductor fabs use as much as 100 megawatt-hours of energy each hour, which is more than many automotive plants or oil refineries do.” They also point out that an average fab will use as much power in a year as 50,000 homes – that’s enough electricity to run a small town. And as the demand for semiconductors continues to grow, and the production process becomes ever more sophisticated, on its current trajectory, the industry’s energy usage is only set to increase.
Electricity production is of course a major source of CO2 emissions, with the majority of power stations still fuelled by hydrocarbons such as coal and gas. It’s an inescapable fact that using more energy means emitting more greenhouse gases – so reducing energy consumption is an obvious way for the semiconductor industry to improve its environmental footprint. But given the demand outlined above, how is this realistically possible?
If we look at where the energy in fabs is actually being used, a solution becomes clearer. McKinsey calculates that approximately 55% of energy consumption is generated by running tools. Given that many of the machines on a fab’s production line are in use 24/7, this perhaps isn’t that surprising. And as I noted earlier, chip companies aren’t about to reduce this level of operation just to become more environmentally-friendly – for both them and other industries, the stakes are too high to do anything that would threaten production.
This is reflected in the KPIs that fabs work to, which are primarily based on cycle time, throughput and yield – energy consumption has historically been very much a secondary consideration. However, production doesn’t completely exist in a bubble, and as with any company, the bottom line is the ultimate driver of operations. Cost reduction is just as important as revenue generation. McKinsey notes that, depending on local electricity tariffs, energy consumption can account for up to 30% of a fab’s operating costs. And with the price of energy continuing to rocket, that figure is almost certain to increase.
So given that energy is a factor that affects both production costs and sustainability, reducing consumption is edging its way up the fab’s table of priorities. Yet what if you could address both those primary KPIs and the need to be greener at the same time?
Optimization technology is the key. It’s a point I keep returning to, but if chip companies are to deal with the challenges that the future is sure to keep throwing at them, then they have to start adopting best-in-class smart manufacturing practices and software.
To properly optimize the way in which the fab works, we have to first understand exactly what the state of the entire WIP is in real time. By mapping the current state of the fab’s operations, it’s possible to identify where bottlenecks are occurring due to sub-optimal scheduling. And in our experience of working with different fabs, the tools where queues usually occur are involved in the most energy-intensive stages of the production process – for example, photolithography, diffusion furnace and in the cleanroom.
Using optimization software to reduce bottlenecks by improving how wafers move through energy-intensive tools, the fab’s primary KPIs can be met and energy consumption at these tools can be reduced. For example, doing more moves with fewer tools at the photo stage means that it’s possible for some tools to be left idle. Or doing the same moves but with fewer batches at the furnace stage means fewer energy-intensive furnace runs.
At the tail end of last year, I gave a presentation at the Smart & Green Manufacturing Summit, as part of Semicon Europa 2022, that outlined how our scheduling optimization software has already achieved these goals in the real world.
Optimization technology can also be used to directly control the energy consumption of less busy tools as well. As long as those areas that are prone to bottlenecks are running efficiently, and all primary KPIs are being met, tools in other areas can be optimized specifically for energy conservation – for instance, powered down because the scheduling technology has identified that they aren’t required or don’t have to be operated at their maximum rate.
Most of the larger semiconductor companies are looking at ways to be greener and meet net zero goals, although carbon offsetting is currently playing more of a role than making manufacturing processes more energy efficient. However, some companies are addressing the issue head-on and looking at ways to control their fabs’ energy use – for example, Flexciton, Imperial College London and the Technical University of Munich are currently working together on a project to map Infineon’s energy consumption, with the aim to better understand how smarter decisions can help the company reduce its carbon emissions.
The demand for semiconductors is only going to grow in the decades ahead, but a reckoning over the industry’s attitude to sustainability will be reached much sooner than that unless energy reduction becomes one of its key priorities. Yet by working smarter and re-evaluating their production processes, it’s absolutely possible for companies to improve throughput and yield while at the same time being more energy efficient.
Author: Jamie Potter, CEO and Cofounder
Join Felipe as he shares his typical day at Flexciton, highlights the most rewarding aspects of his role and offers valuable career advice in this month’s edition of The Flex Factor.
Join Felipe as he shares his typical day at Flexciton, highlights the most rewarding aspects of his role and offers valuable career advice in this month’s edition of The Flex Factor.
I’m an Optimization Engineer, which deals with mathematical optimization and software engineering. At work, you’ll find me working on new components to our optimization model, thinking of and/or implementing improvements and fixing some bugs that appear from time to time. In general, it involves understanding the semiconductor manufacturing process and writing and maintaining production code to incorporate mathematical optimization into our software so that we can deliver the best schedules for our clients.
Treat myself with a cappuccino before anything else (I always regret it at the end of the month, it’s an expensive addiction), then I’m ready for our daily stand-up. That’s when the team meets to discuss priorities, status of ongoing work, if there are any blockers and how to sort them. After that, it is a mix of coding (new feature, improvement, bug fix, etc), discussing the design of a new implementation with another team member and doing code reviews. From time to time, I also present something in our knowledge transfer sessions and have also been onboarding new starters on the topic of optimization.
We deal with very complex problems, so it really is a mix of challenging and exciting work, all done within a friendly and supportive environment! Learning a lot and having fun ends up being a byproduct.
Interesting, fun, challenging.
Tasks that initially seem daunting and make you doubt your knowledge and expertise are often the ones that will make you grow.
I guess I’ll have to pick more than one here. It may sound cliche or cringe, but the first day was one of my best memories. Moving abroad for a new job and to do something for the first time is quite an intimidating experience. So it was a great feeling when I had a warm welcome on my first day. Everyone was friendly, open and looked super smart.
Apart from that:
Are you interested in working for Flexciton? Head over to our careers page to check our current vacancies or connect with us.
Jamie shares his thoughts on the UK’s £1bn semiconductor strategy, why he thinks there's untapped potential with disruptive technology, and how the UK’s abundant talent pool could be the key for our growth in the global industry.
Rishi Sunak’s recent unveiling of the UK’s £1bn ($1.3bn) semiconductor strategy was always bound to provoke a reaction from critics. In an attempt to improve research and development and bolster international cooperation, the UK announced it will partner with Japan as part of its strategy. The aim of this collaboration is to foster knowledge sharing, increase expertise, and mitigate supply chain risks. The obscurity of the government’s strategy – as well as the delay from the original announcement date of autumn last year – tells me that they are very much still figuring this out. It appears the next step is to employ an advisory panel to help decipher what the actual actions will be before autumn of this year. A full year after the original date. Fundamentally, though, I think the UK has got this one right. It’s too late for us to start throwing huge amounts of money at building fabs, since we simply don’t have the capital or the resources to create our own security of supply. Instead, it’s much more beneficial for us to focus on specialisms that could make us globally relevant to the supply chain. However, what I’m less convinced about, is the government's understanding of the areas of expertise we already possess.
Let’s look at where the UK is particularly strong, as with a limited budget, focusing on creating that specialism makes sense. The obvious one here is chip design, which was detailed in the strategy unveiling last week. ARM has been at the forefront of this market for many years and, along with the spin-offs coming from the University of Cambridge, it’s a sector where the UK could be considered at the forefront. Other nations, such as China, have been offering a greater deal of support to their design companies for many years now, so it makes sense to match them if we want to remain competitive. Another obvious one is innovative new software and technology, which is not detailed in the government’s strategy. The skills shortage means that emerging technology like artificial intelligence will soon have to play a more central role in wafer fabs as they transition towards smart factories. We have a faster growing tech hub here than anywhere else in Europe, putting the UK in a prime position to establish itself as a global leader in smart manufacturing technology. Yet even with this opportunity sitting directly under their noses, I don't think the government has yet realised its potential.
For those who are unfamiliar, smart manufacturing refers to the integration of advanced technologies like artificial intelligence and automation into manufacturing processes and systems. It has the potential to transform traditional factories into intelligent, data-driven environments that enable much higher levels of efficiency with fewer skilled people required. Now, smart manufacturing is still very much an emerging field. At this point, only a handful of leading-edge manufacturers are concerned with it and even fewer have begun actually adopting it. But the current challenges faced by the industry, such as the skills shortage, are making its importance ever-more apparent.
The government seems to think that the best way to solve the skills shortage is to invest in the education of relevant fields. There’s no doubt that this will help somewhat, but it’s going to take a very long time. What they fail to take into consideration is that working in semiconductors used to be one of the most exciting prospects for skilled engineers. In some cases, it still may be, but now it has to compete with working for companies like Google or Apple. So as the demand for people rises with the construction of new fabs and tech companies continue to attract graduates, it’s going to be a challenge to attract the level of talent the industry needs in the time it needs it. As many of the vanguard wafer fabs are realising, a quicker and more realistic approach to solving the skills shortage is implementing smart manufacturing technologies.
The key component of smart manufacturing is software. The tech startup ecosystem here in London has a value of over £250bn ($314bn), which is over triple that of the next largest in Europe. The UK government is well aware that novel technology is a domain that the UK – and London in particular – is well positioned to become a leader in. But it seems they haven’t yet figured out that our strengths in this area could be applied to our semiconductor strategy.
The talent pool of software and data engineers we have access to here in London rivals that of anywhere else in the world. It’s one of the main drivers behind the capital’s success as a tech hub. With support from the government, this abundance of skilled engineers and software companies could be harnessed to create a specialism in smart manufacturing technology. Many of the disruptive technologies that will be used in wafer fabs over the next 20 years will come from outside of the traditional semiconductor supply chain, many of which could be already operating in London today. All of this means that the foundations for this new specialisation are already laid, giving the UK a head start to become a global leader in smart manufacturing.
To conclude, the UK's semiconductor strategy reveals both missed opportunities and potential for growth. While the government's collaboration with Japan and investments in chip design are steps in the right direction, our potential with emerging technologies seems overlooked. The UK's thriving tech hub, particularly in London, presents a pool of software companies and skilled engineers that could be leveraged to establish the country as a leader in smart manufacturing technology. By embracing smart manufacturing, the UK can help address the skills shortage, drive efficiency in the industry, and secure a position of relevance in the global semiconductor supply chain. However, it remains crucial for the government to recognise and harness these existing strengths to fully realise the potential for growth and competitiveness in the semiconductor industry.
Author: Jamie Potter, CEO and Cofounder
In Part 2 of this blog, Ben Van Damme delves further into the potential of advanced optimization-based scheduling for wafer fabs in the not too distant future.
In Part 1 of this blog, we focused on use cases where lots are scheduled on tools and how advanced scheduling gives users the ability to optimize for future decisions as well as real-time. When we say "advanced," we are referring to autonomous, optimization-based solutions. Our emphasis was primarily on how scheduling can enhance productivity in a fab today. In Part 2, however, we’ll delve further into its potential for fabs in the not too distant future.
Previously, I discussed how task lists are typically associated with human workers. However, it is worth noting that task lists can also be applied to automated systems such as automated guided vehicles (AGVs) and automated material handling systems (AMHS) with the use of an advanced scheduler. With task lists, an advanced scheduler can not only determine which lot is assigned to which tool and when, but also which operator – or robot – will be serving the tool. There’s a whole set of new opportunities that arise with that, as humans and robots, just like tools, have a limited capacity that can be optimally utilised. It’s clear then that the possibilities for advanced scheduling go beyond the stand-alone Industry 4.0 applications and have the potential to integrate vast amounts of fab data into a holistic system.
One of the use cases of such a holistic system is described later on in this blog as a type of ‘digital twin’, but the capabilities of an advanced scheduling system go beyond that. With a digital twin concept, the human is still very much inside the cockpit. An advanced scheduling system, on the other hand, is more like an autopilot, augmenting the capabilities of other systems and taking control of manufacturing decisions when necessary. As such, advanced scheduling is a cornerstone of the so-called ‘smart factory’. Let’s try to understand the huge array of benefits it can bring. First, we’ll cover a couple of use cases that can benefit the manufacturers. Second, we’ll share some thoughts on how advanced scheduling aligns with the idea behind Industry 5.0 and how the technology can serve ourselves as humans.
Once a lot is intelligently scheduled, we know when to process it and on which tool. The lot can be transported to that tool’s specific staging rack just before it gets processed. It enables fabs to eliminate waste by optimizing transport capacity, which removes the likelihood of a lot being transported at half capacity only for it to wait in queue. Transport scheduling also enables splitting logistics and processing workflows; some workers focus on keeping the tools running, others focus on getting the lots to the tools in time. Multi-cleanroom fabs will make better use of their capacity in areas that for logistical reasons are not preferred. Which means no more remote idle machines waiting for a lot that doesn’t arrive.
With better control of lot processing, intra-fab logistics, and workforce planning, we get a more realistic view on the true capacity of a factory. We call it a dynamic capacity model, resembling the idea of a digital twin of a production plant. A dynamic capacity model better reflects the current state, loading and dynamics in a factory, as opposed to the static capacity models commonly used. Until now in wafer fabs, dynamic capacity models have at best been approximated by what-if scenarios in simulation models, but the potential goes beyond that. When playing around with different scenarios – e.g. when to plan maintenance or shutdowns, which availability increase has the most impact on the whole factory, what’s the effect of frequent product mix changes, what lead times to expect and so on – it should allow factories to better judge the impact of their decisions. Optimization can even help by not only interpreting the outcome, but suggesting the best decision for a fab’s goals.
Eventually, dynamic capacity models could scale to corporate level in multi-factory models. Further up, these models could feed into supply chain planning software. During the supply chain crisis, it was striking to see how disconnected sales and operations planning cycles in semiconductors were from the actual operational challenges of factories. Part of it was because of models that don’t properly comprehend the actual situation the factory was in. Fabs were treated as black boxes with a simple input and output signal, but just because you have promised your customers a sooner delivery date, it doesn’t mean it will happen automatically. You need a driver towards that new target, and that’s where advanced scheduling software helps, by optimizing towards shorter lead times. Its integration into dynamic capacity models and supply chain planning software would lead to more reliable input for inventory and order fulfilment optimization engines. This translates into lower inventory costs and better delivery performance of a company.
Eventually, we want technology to help us overcome the challenges we face as humans. From what has been written so far, this blog might give the impression that this technology is primarily serving profitability. But becoming a smart factory doesn’t necessarily contradict with a human-centric approach. Industry 5.0 is the theoretical concept that’s been introduced for that. It counters the illusion that the future of manufacturing is one in which humans play a minor role. Instead, we should embrace both the capabilities of new technologies, as well as those of humans and find synergies to make the best of both worlds. While Industry 4.0 can do a great job in automating repetitive tasks or making sense out of masses of data, humans have the advantage of better interpretation of context, require fewer data points to understand, and can make value trade-offs. Humans will not miraculously disappear from the factory shop floor, so we’ll benefit from thinking about how these advanced technologies can harmoniously coexist with people and yield mutually beneficial outcomes.
The obvious fear with advanced scheduling is that operators and technicians will turn into de facto robots, where only adherence is of importance when aiming to get more out of the workforce. Let’s turn that thought up-side-down: what if the same work could be better distributed amongst the team by offloading peaks to underloaded co-workers? Advanced scheduling can better predict and hence properly distribute work aligned with an individual's availability and level of training. Also the workflow itself - the number and order of actions to perform - can be streamlined to lower physical and mental workload.
With detailed production schedules, any lack of staff or training becomes directly visible and quantifiable. Hiring and training programs could become more timely and data-driven, just as annual evaluations will become less subject to biases of the manager. Even on-the-spot productivity can be monitored and optimised. This may sound like a “Big Brother” concept, but compare it with the advancement of sports analytics and medicine in the last decade. Professional athletes don’t complain about data integrity and privacy issues, because (1) it’s part of their job and (2) it helps them in what they want to achieve. If athletes ignore their data, they simply don’t reach the top anymore. Similarly, the fourth and fifth industrial revolution will bring staffing to higher levels of productivity, not because they are squeezed out more, but because the data will reveal where there’s room for improvement or when a red line is about to be crossed.
Given the increasing scale and complexity described above, significant computational power and data storage capabilities will be necessary. This makes it likely that cloud-based technology will be adopted to facilitate the transition to smart factories. Although many fabs are currently far from achieving smart factory status, it is clear that the industry is moving in this direction. Therefore, factory managers must acknowledge that the transition to becoming a smart factory is not just a concern for the future and must be implemented within a realistic timeframe. The foundations for this transition, including employee readiness, are already being established today. And given the use cases discussed, let there be no doubt that advanced scheduling will play an integral part in the next generation of wafer fabs.
Author: Ben Van Damme, Industrial Engineer and Business Consultant