Part 2

In the first part of 'C for Cycle Time', we explored the essence of cycle time in front-end wafer fabs and its significance for semiconductor companies. We introduced the operating curve, which illustrates the relationship between fab cycle time and factory utilization, as well as the power of predictability and the ripple effects cycle time can have across the supply chain. 

In part 2, we will explore strategies to enhance cycle time through advanced scheduling solutions, contrasting them with traditional methods. We will use the operating curve, this time to demonstrate how advanced scheduling and operational factors, such as product mix and factory load, can significantly impact fab cycle time. 

How wafer fabs can improve cycle time 

By embracing the principles of traditional Lean Manufacturing, essentially focused on reducing waste in production, cycle time can be effectively reduced ^[1]. Here are a few strategies that can help improve fab cycle time: 

• Improving maintenance strategies, for example moving from reactive to more proactive maintenance can improve cycle time with fewer breakdowns and more predictable tool availability ^[2]. 
• As noted in part 1, minimizing wasted time in batch formation and reducing the frequency of rework due to defects improves cycle time.  
• Purchasing faster tools. Although, this can be a time-consuming and costly undertaking. In-facility expansion may take up to a year, while the commencement of a new facility could extend to three years ^[3].
• Establishing optimal batching in diffusion poses a considerable challenge, given the intricate process constraints within the diffusion area, such as timelinks, as we’ve explained in a recent blog.
• Balancing cycle time of hot lots with average fab cycle time. Fabs often assign higher priority to hot lots, which can negatively impact the average cycle time of production lots ^[4].
• Developing the skills of existing operators and expediting the onboarding process for new operators could be another means of reducing variability in production, thus impacting cycle time.
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The implementation of an advanced AI scheduler can facilitate most of the strategies noted above, leading to an improvement in cycle time with significantly less effort demanded from a wafer fab compared to alternatives such as acquiring new tools. In the next sections we are going to see how this technology can make your existing tools move wafers faster without changing any hardware!

Applying an advanced AI scheduler to improve cycle time 

In this section, we delve into how an advanced AI scheduler (AI Scheduler) can maintain factory utilization while reducing cycle time. 

First let’s define what an AI Scheduler is. It is an essential fab software that has a core engine powered by AI models such as mathematical optimization. It possesses the ability to adapt to ongoing real-time changes in fab conditions, including variations in product mixes, tool downtimes, and processing times. Its output decisions can achieve superior fab objectives, such as improved cycle time, surpassing the capabilities of heuristic-based legacy scheduling systems. More aspects of an advanced AI scheduler can be found in our previous article, A is for AI. The AI Scheduler optimally schedules fab production in alignment with lean manufacturing principles. It achieves this by optimally sequencing lots and strategically batching and assigning them to tools. 

Figure 5 shows an example of how an AI Scheduler can successfully shift the cycle time from the original operating curve closer to the theoretical operating curve. As a result, cycle time is now 30 days at 60% factory utilization. This can be accomplished by enhancing fab efficiency through measures such as minimizing idle times, reducing re-work, and mitigating variability in operations, among other strategies. In the next sections, we will show two examples in metrology and diffusion how cycle time is improved with optimal scheduling. 

Reducing queuing times and tool utilization variability in metrology 

Many wafer fabs employ a tool pull-system for dispatching. In this approach, operators typically decide which idle tool to attend to, either based on their experience or at times, randomly. Once at the tool, they then select the highest priority lots from those available for processing. A drawback of this system is that operators don't have a comprehensive view of the compatibility between the lots awaiting processing, those in transit to the rack, and the tools available. This limited perspective can lead to longer queuing times and underutilized tools, evident in Figure 6.

An AI Scheduler addresses these inefficiencies. By offering an optimized workflow, it not only shortens the total cycle time but also minimizes variability in tool utilization. This in turn indirectly improves the cycle time of the toolset and overall fab efficiency. For example, Seagate deployed an AI Scheduler to photolithography and metrology bottleneck toolsets that were impacting cycle time. The scheduler reduced queue time by 4.3% and improved throughput by 9.4% at the photolithography toolset ^[5]. In the metrology toolset, the AI Scheduler reduced variability in tool utilization by 75% which resulted in reduced cycle time too, see Figure 7 ^[6].

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Improving cycle time and optimal batching in diffusion

Diffusion is a toolset that poses operational complexities due to its intricate batching options and several coupled process steps between cleaning and various furnace operations ^[7]. Implementing an AI Scheduler can mitigate many of these challenges, leading to reduced cycle time:

1. Strategic Batching can reduce total cycle time in diffusion. To maximize the benefit of an AI Scheduler, the fab should provide good quality data.
2. Automated Furnace Loading: Typically, diffusion loading is accomplished via a pull-system from the furnace. This means that operators would revisit the cleaning area to manually pick the best batches, based on upcoming furnace availability. This approach often demands substantial resources and time, thereby increasing cost or cycle time. The AI Scheduler curtails this time considerably, freeing up operators for other essential tasks, which indirectly may reduce cycle time elsewhere.
3. Reduction of Timelinks Violations: A recent pilot implementation of an AI Scheduler in diffusion at a Renesas fab underscored its effectiveness. As displayed in Figure 8, timelink violations were significantly reduced. This minimizes the necessity for rework, further cutting down the cycle time, as explained earlier in the article.
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Maximizing the value of the AI Scheduler by integrating with other applications

In the above examples of photo, metrology and diffusion toolsets, the AI Scheduler can support operators to achieve consistently high performance. To enhance the efficiency of the scheduling system in fabs predominantly run by operators with minimal AMHS (Automated Material Handling Systems) presence, pairing the scheduler with an operator guidance application, as detailed in one of our recent blogs on user-focused digitalisation, can be a valuable approach. This software will suggest the next task required to be executed by an operator. 

The deployment of an AI Scheduler should focus on bottleneck toolsets - specifically, those that determine the fab's cycle time. Reducing the cycle time of a toolset will be inconsequential if that toolset is not a bottleneck. Consequently, fabs should consider the following two approaches:

1. Ensure the deployment of the AI Scheduler on the most critical toolsets to effectively address dynamic bottlenecks. This ensures that as bottlenecks shift, the AI Scheduler can promptly reduce the cycle time of the newly identified bottlenecked toolset. By doing so, fabs can consistently maintain a low cycle time.
2. The introduction of a global (or a fab wide) application layer – such as a solution that looks across all the toolsets and all lots across the whole line – can help coordinate all deployed AI Schedulers. This application should indicate which toolsets are bottlenecks and it should also adjust lot priorities or production targets per toolset to ensure a smooth flow across the line. The interaction between global applications and local scheduling applications can be seen in recent papers ^{[9] [10]}. 

Dealing with dynamic changes in the fab and understand trade-offs between competing objectives

Another factor to consider is that the actual operating curve of the fab is moving constantly based on changes in the operating conditions of the fab. For example, if the product mix changes substantially, this may impact the recipe distribution enabled in each tool and subsequently, the fab cycle time vs factory utilization curve would shift. The operating curve can also change if the fab layout changes, for example when new tools are added.

In Figure 9, we show an example wherein the cycle time versus factory utilization curve for product mix A shifts upward. This signifies an increased cycle time in the fab due to the recent changes in the product mix (and the factory utilization was slightly reduced under these new conditions). An autonomous AI Scheduler, as described by Sebastian Steele in a recent blog, should be able to understand the different trade-offs. For example, in Figure 10, the AI Scheduler could deal with the same utilization as before (60%) with product mix A, but the cycle time will stay at 50 days (10 days more than in the case with product mix A). Another alternative is that the user can then decide if they want to customize this trade-off so that the fab can move back to the same cycle time with this new product mix B at 40 days but staying with lower utilization at 57%. 

Trade-offs between different objectives at local toolsets may impact the fab cycle time. Consider the trade-offs in terms of batching costs versus cycle time. For instance, constructing larger batches might be crucial for high-cost operational tools such as furnaces in diffusion and implant. However, this approach could lead to an extended cycle time for the specific toolset and, consequently, an overall increase in fab cycle time. 

Tool availability and efficiency significantly affect cycle time, akin to the influence of product mix on operating curves. If tools experience reduced reliability over time, the operating curve may shift upward, resulting in a worse cycle time for the same utilization. While the scheduler cannot directly control tool availability, strategically scheduling maintenance and integrating it with lot scheduling can positively impact cycle time. A dedicated future article will delve into this topic in more detail.

Conclusion

The topic of the cycle time has been enriched with the introduction of an AI Scheduler, bringing a paradigm shift in how we perceive and manage the dynamics of front-end wafer fabs. As highlighted in our exploration, these schedulers do more than just automate – they optimize. By understanding and predicting the nuances of operations, from tool utilization to lot prioritization, advanced AI schedulers provide a roadmap to not just manage but optimize cycle time considering alternative trade-offs. In future articles we will talk about how scheduling maintenance and other operational aspects can be considered in a unified and autonomous AI platform that we believe would be the next revolution, after the innovations from Arsenal of Venice, Ford and Toyota. 

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Author: Dennis Xenos, CTO and Cofounder, Flexciton

References

• [1] James P. Ignizio, 2009, Optimizing Factory Performance: Cost-Effective Ways to Achieve Significant and Sustainable Improvement 1st Edition, McGraw-Hill, ISBN 978-0-07-163285-0
• [2] Lean Production, 2023, TPM (Total Productive Maintenance), URL.
• [3] Ondrej Burkacky, Marc de Jong, and Julia Dragon, 2022, Strategies to lead in the semiconductor world, McKinsey Article, URL. 
• [4] Philipp Neuner, Stefan Haeussler, Julian Fodor, and Gregor Blossey, 2023, Putting a Price Tag on Hot Lots and Expediting in Semiconductor Manufacturing. In Proceedings of the Winter Simulation Conference (WSC '22). IEEE Press, 3338–3348.
• [5] Robert Moss, Dennis Xenos, Tina O’Donnell, 2023, Deployment of an Advanced Photolithography Scheduler at Seagate Technology, IFORS News, Volume 18, Issue 1, ISSN 2223-4373, pp. 8–10, URL.
• [6] Robert Moss, 2022, Ever-decreasing circles: how iterative modelling led to better performance at Seagate Technologies. Euro 2022 Conference, Finland, URL
• [7] Thomas Beeg, 2023, Impact of “time links” or controlled queue times, Factory Physics and Automation, URL.
• [8] Jamie Potter, 2023, Fab scheduling is now so complex that it needs next-generation intelligent software, Silicon Semiconductor Magazine, Volume 44, Issue 2, pp. 26-29, URL.
• [9] I. Konstantelos et al., 2022, "Fab-Wide Scheduling of Semiconductor Plants: A Large-Scale Industrial Deployment Case Study," 2022 Winter Simulation Conference (WSC), Singapore, pp. 3297-3308, doi: 10.1109/WSC57314.2022.10015364.
• [10] Félicien Barhebwa-Mushamuka. 2020,  Novel optimization approaches for global fab scheduling in semiconductor manufacturing. Other. Université de Lyon. English. ⟨NNT : 2020LYSEM020⟩. ⟨tel-03358300⟩

This two-part article aims to explain how we can improve cycle time in front-end semiconductor manufacturing through innovative solutions, moving beyond conventional lean manufacturing approaches. In part 1, we will discuss the importance of cycle time for semiconductor manufacturers and introduce the operating curve to relate cycle time to factory utilization. Part 2 will then explore strategies to enhance cycle time through advanced scheduling solutions, contrasting them with traditional methods.

Part 1 

Why manufacturers care about cycle time 

Cycle time, the time to complete and ship products, is crucial for manufacturers. James P. Ignazio, in Optimizing Factory Performance, noted that top-tier manufacturers like Ford and Toyota have historically pursued the same goal to outpace competitors: speed ^[1]. This speed is achieved through fast factory cycle times.

This emphasis on speed had tangible benefits: Ford, for instance, could afford to pay workers double the average wage while dominating the automotive market. The Arsenal of Venice's accelerated ship assembly secured its status as a dominant city-state. Similarly, fast factory cycle times were central to Toyota’s successful lean manufacturing approach.

Furthermore, semiconductor manufacturers grapple with extended cycle times that can often span 24 weeks ^[2]. This article will focus on manufacturing processes in front-end wafer fabs as their contribution to the end product, such as a chip or hard drive disk head, spans several months. In contrast, back-end processes can be completed in a matter of weeks ^[3]. However, the principles discussed apply universally to back-end fabs without sacrificing generality.

Why Short Cycle Times Matter for Front-end Wafer Fabs

• Revenue acceleration: The quicker products reach customers, the faster revenue streams in. However, quantifying the precise financial impact due to cycle time is intricate and beyond this article's scope.

• Competitive advantage: Gaining a competitive advantage involves reducing cycle time in R&D wafers, which accelerates product launches. More than 20% of front-end fab production lines can be used for R&D wafer testing and iteration. Swift deliveries enhance a company's reputation, leading to more contracts. At the 2022 Winter Simulation Conference, Micron highlighted their rapid advancements: maturing 30% quicker in DRAM (five months ahead of the previous node) and 20% in NAND (a year faster than the prior node). See Figure 1.

• Agility in market responsiveness: A fab with shorter cycle times can swiftly adjust to market fluctuations, whether that is a surge in demand or a shift in product preferences, such as changes in product mix. It can also respond faster to changes in customer requirements.

• Risk mitigation: The shorter the cycle time, the quicker a fab can respond once defects have been detected as it takes less time to perform rework.

• Inventory management: Lower cycle times can reduce the amount of work-in-progress (WIP) in buffers or racks (intermediate stock), or stock at the end of the production line. This not only liberates tied-up capital but also wafers can move quicker with less WIP in the fab as it is shown in a later section introducing the operating curve.
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Achieving Predictable Cycle Time

Less variability in cycle time helps a wafer fab to achieve better predictability in the manufacturing process. Predictability enables optimal resource allocation; for instance, operators can be positioned at fab toolsets (known as workstations) based on anticipated workload from cycle time predictions. Recognizing idle periods of tools allows for improved maintenance scheduling which will result in reduction in unplanned maintenance. In an upcoming article (Part 2), we'll explore how synchronizing maintenance with production can further shorten cycle times.

Measuring and monitoring the cycle time improves overall fab performance

Measuring and monitoring cycle times aids in identifying deviations from an expected variability. This, in turn, promptly highlights underlying operational issues, facilitating quicker issue resolution. Additionally, it assists industrial engineers in pinpointing bottlenecks, enabling a focused analysis of root causes and prompt corrective actions.

Supply chain stakeholders usually fail to understand the impact of cycle time 

In the semiconductor industry, cycle time plays a pivotal role in broader supply chain orchestration:

• A predictable cycle time informs suppliers when to provide fresh batches of raw materials. 
• Furthermore, it influences the downstream processes of Assembly & Test Operations (back-end facilities). Back-end facilities with a cycle time of less than a week gain enhanced predictability, allowing for more effective allocation of capacity and resources.
• Predictable cycle times will also inform safety inventory levels, freeing capital and optimizing storage space. 
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Cycle time is a component of the total lead time of a product (it also includes procurement, transportation, etc). Therefore, total lead time can be reduced if the long cycle times in the front-end wafer fabs are reduced. A reliable cycle time nurtures trust with suppliers, laying the foundation for favorable partnerships and agreements. In essence, cycle time is not just about production; it's the heartbeat of the semiconductor supply chain ecosystem.

Understanding how cycle time impacts product delivery times is essential for the semiconductor industry. In some analyses, you could see that cycle time is confused with capacity, as the authors in a McKinsey article stated “Even with fabs operating at full capacity, they have not been able to meet demand, resulting in product lead times of six months or longer” ^[4]. On the contrary, in a fab operating at full capacity, lead times of the products will increase as the average cycle time of manufacturing is increasing. 

How to measure cycle time

Fab Cycle Time

The fab cycle time metric defines the time required to produce a finished product in a wafer fab. The general cycle time term is also used to measure the time required to complete a specific process step (e.g. etching, coating) in a toolset, known as process step cycle time. The fab cycle time consists of the following time components as can be seen in Figure 2:

• Value-added processing time, which is the time taken to transform or assemble the unfinished product, which is a wafer in our case.

• Non-value-added processing time includes the time taken for inspection and testing, as well as the time for transferring the wafer between different steps.

• Time to prepare the products for processing: this refers to the time operators or tools required to form a batch, i.e. to select which lots should be bundled together for processing.

• Queue time: the time spent where the unfinished wafer is waiting to be processed because the tool required is busy, due to the tool processing another batch or undergoing maintenance.
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To measure and monitor cycle time, wafer fabs must track transactional data for each lot, capturing timestamps for events like the beginning and completion of processing at a tool. This data is gathered and stored by a Manufacturing Execution System (MES). Such transactional information can be utilized for historical operations analysis or for constructing models to forecast cycle times influenced by different operational factors. This foundation is crucial for formulating the operational curve of the fab, which we'll delve into in the subsequent part of this blog. As outlined in an article by Deenen et al., there are methods to develop data-driven simulations that accurately predict future cycle times ^[3].

Fab operating curve: Fab Cycle Time versus Factory Utilization

As we mentioned earlier, historic data can be used to generate the operating curve of a fab which describes the cycle time in relation to the factory utilization. Figure 3 shows the graph of the fab cycle time in days versus the utilization of the fab (%). The utilization of the fab is defined as the WIP divided by the total capacity of the fab. 

We have found this method useful in understanding the fundamental principles of cycle time. The operating curve helps to explain how factory physics impact fab KPIs such as cycle time and fab utilization by showing the changes in the operating points: 

• The horizontal line, representing the summation of raw process times (known as theoretical cycle time), envisions a scenario with zero queuing time in the fab. This illustrates the impact of queuing time on cycle time as we increase WIP, moving right on the x-axis. Accumulation of queuing time becomes inevitable with the introduction of more WIP in the fab.
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• The ideal operating curve represents the operation of a wafer-fab assuming that there is zero waste. This curve defines the minimum achievable cycle time for each fab load and the difference between this curve and theoretical cycle time is because of real life variability in the fab that cannot be eliminated completely, e.g. unplanned maintenances, inconsistent tool processing times.
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• The cycle time tends to go to infinity, when you move towards 100% utilization of the fab.
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• The actual operating curve, cycle time versus factory utilization, represents the current fab’s operation considering all the inefficiencies such as excessive inventory, variability in operations, idle times, poor batching and rework.
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• Both curves assume average or constant values of the operational parameters of the fab for example a fixed number of tools installed, an average availability of each tool and labor, and a constant product mix.
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• The actual operating curve describes the impact on cycle time if we load the fab with more WIP as shown in Figure 4. The fab management could use this information to make a decision about the trade-off between cycle time and factory utilization. Higher fab utilization is associated with a higher throughput (i.e. number of wafers per unit of time).

In Figure 3, you can see that the current fab cycle time is 40 days when the factory utilization is at 60%. Theoretically, we could reduce the cycle time to 22 days. The difference between these two points is due to the inefficiencies that contribute to the factory cycle time as explained in the introduction of this section. In Part 2 of this blog, we will explore the various types of inefficiencies and examine how innovation can shift the operating curve to achieve lower cycle times while maintaining the same fab utilization.

Summary 

In summary, cycle time is not merely a production metric but the very pulse of the semiconductor manufacturing and supply chain. It governs revenues, shapes market responsiveness, and is pivotal in driving innovation. By understanding its nuances, semiconductor companies can not only optimize their operations but also gain a competitive edge. And while we've scratched the surface on its significance, the question remains: how can we further reduce and refine it? In part 2 of the C for Cycle Time blog, we will discover innovative techniques that promise to revolutionize cycle time management in wafer fabs.

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Author: Dennis Xenos, CTO and Cofounder, Flexciton

References

• [1] James P. Ignizio, 2009 ,Optimizing Factory Performance: Cost-Effective Ways to Achieve Significant and Sustainable Improvement 1st Edition, McGraw-Hill, ISBN 978-0-07-163285-0
• [2] Semiconductor Industry Association, 2021, Blog, URL
• [3] Deenen, P.C., Middelhuis, J., Akcay, A. et al., 2023, Data-driven aggregate modeling of a semiconductor wafer fab to predict WIP levels and cycle time distributions. Flex Serv Manuf J. https://doi.org/10.1007/s10696-023-09501-1
• [4] Ondrej Burkacky, Marc de Jong, and Julia Dragon, 2022, Strategies to lead in the semiconductor world, McKinsey Article, URL. 

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Welcome to a nuanced exploration of pivotal considerations surrounding cloud adoption in the context of wafer fabrication. For those reading sceptically, uncertain about the merits of cloud integration, or perhaps prompted by concerns about lagging behind competitors—this blog endeavours to shed light on key areas of relevance.

Introduction

For those reading this blog, the chances are you (or perhaps your boss) remain unconvinced about the merits of cloud adoption, yet are open to participating in the ongoing debate. Alternatively, there might be a concern of falling behind industry peers, perhaps heightened by recent security incidents such as the hacking of X-Fab. By the end of this short article, you will have gained valuable insights into the significant areas of cloud security, with the anticipation that such information will contribute to a more informed decision-making process.

Firstly, this is about using a cloud service, not running your own systems in the cloud. There are good arguments for that too, but that’s not what this article is about. So, the areas deemed worthy of exploration within this context include:

• Security - It is a paramount concern that influences the reluctance of fabs to embrace cloud services, often accompanied by apprehension about entrusting sensitive data to cloud platforms.
• Type of service - If you’re running it on-premise, you’re probably managing it. Would you prefer to be buying software or a service?
• Cost - One of the supposed benefits of the cloud is less overall running cost. How much truth is in that? 
• Reliability and criticality - What about backups and disaster recovery and failover and downtime and maintenance and…?

Recognising the complexity of these topics, we aim to take a segmented approach, with this blog dedicating its focus to the critical factor of security. Subsequent entries promise a comprehensive discussion on the remaining aspects. 

Security

We’re going to start with a simple one. Is your fab in any way connected to the internet? If you’re genuinely air-gapped, then it's reasonable to assume you already have a high level of security. But, if you’re not actually air-gapped, then you could actually improve your security by using a cloud service rather than running that service on-prem. Not instantly obvious perhaps, but let us explain. 

My fab is connected to the internet already

The most compelling argument that exists for this is a simple one. Microsoft, AWS, IBM and Google all run respectable professional public clouds. If the service we’re talking about connecting to runs on any one of them, it’s fair to say they have similar approaches to cybersecurity.

Microsoft alone employs 3500 cybersecurity professionals to maintain the security of Azure and together they spend a lot on cybersecurity improvements. That’s an awful lot more person-hours on security than most are going to be able to apply from their team. Every single one of them is contributing to the security of a system running in their cloud.

“Aha!”, you say, “that tells me that the underlying public cloud infrastructure that the service is running on is probably as secure as anything connected to the world could be, but that doesn't mean that the service running on it is, right?” And yes, that’s a fair concern. As one of those service providers, we can confirm that we do not employ 3500 cybersecurity professionals. But because we run our service on Azure, we don’t need to. More than half our fight is already done for us and the remainder is a lot easier. For example:

• Staying on top of security patches: Not something we need to worry about. Azure does almost all of it for us. The only thing it doesn’t do is update our own code dependencies, so we have Snyk do that for us.
• Anti-virus: Don’t want viruses in your systems? Tick. That’s an easy built-in feature.
• Secure data: All storage is encrypted without us having to do anything special and we can do the same with data in transit.
• Secret management: There’s a whole infrastructure designed by those 3500 cybersecurity professionals that we can use right off the shelf.
• Access management: Active Directory–a directory service built by Microsoft–is built into everything so we just have to define what the roles should be and who has access. There’s not many roles, and only some very trusted people.
• Security auditing: Every action in every piece of infrastructure is logged by default and we can feed that all in very easily to our security monitoring systems to identify any suspicious activity.

In discussing the ease of these security measures, perhaps we’ve been slightly frivolous. However, despite the casual tone, the implementation of security measures when using cloud technologies is notably simpler when compared with organisations that manage their own hardware. 

My fab is air-gapped right now

On the other hand, maybe you’re a fab that is actually air-gapped. You’ve got a solid on-site security team and excellent anti-social-engineering measures. Why introduce any risk? Fair question. We’d argue that this is going to become an increasingly challenging problem for you and maybe now’s the time to get ahead of the problem. Tools on your shop floor are already getting more modern, with virtualised metrology and off-site telemetry feeds for predicting failure rates using machine learning. Some of these systems just can’t be run on site and you’ll increasingly have to do without the more advanced aspects of your tooling to maintain your air gap. Over time this will take its toll, and your competitors will begin to pull away.

At this point it’s worth mentioning that SEMI has put together standards in the cybersecurity space. These address risks like bringing tools into your network with embedded software on them as well as defining how to set up your fab network to secure it, while still enabling external communication. We’d suggest that you should treat a cloud service no differently. It is entirely possible to use a managed service, in the cloud, connected to your fab, while still relying on purely outbound connectivity from your fab, leaving you entirely in control of what data is provided to the service and what you do with any data made available by that service in return.

In summary

If you’re already “internet-enabled” in your fab, then we’d argue that using a reputable public cloud service is actually more secure than running that same service on-prem.

If you’re completely offline, we’re not going to argue that using a cloud service is more secure than not connecting to the internet. What I am arguing though, is that at some point you’re going to have to anyway, so you’re better off getting on top of this now rather than waiting until you’re forced into it by the market. 

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Author: Ray Cooke, VP of Engineering at Flexciton

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