Goodhart’s Law and the Pitfalls of Targeting Load Port Utilisation on Photo Tools
It has been described as the law that rules the modern world, and its effects can be observed in every organisation. I’m referring to Goodhart’s law, named after British economist Charles Goodhart, who wrote the maxim: “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”
A common flavour of this effect is described in the following cartoon, based on a possibly apocryphal story of how central planning failed in a nail factory in the Soviet Union.
We have seen (less dramatic) examples of this effect at work in semiconductor wafer fabs. For instance, teams of operators may be measured on the number of lot moves that occur during their shift. In general, more moves per shift correlates with more wafers delivered on time to customers. However, this relationship breaks down if operators ‘game the system’ by loading batch tools with small batches at the end of the shift, thus wringing out a few extra moves in their shift, but hobbling the next shift.
Memorable though such examples are, they give the impression that Goodhart’s Law relies on people being uninterested in the ultimate goal that their organisation is pursuing. However, apathy is not usually the driving factor in Goodhart’s law; whenever lack of information, limited computational power or even an inability to concisely express our true preferences leads us to substitute a proxy metric for our true goal, the law is bound to rear its head. Former Intel CEO Andy Grove described the effect of such surrogate indicators as like “riding a bicycle: you will probably steer where you are looking”; and if where you’re looking isn’t perfectly correlated with the road ahead, you can expect a wobbly ride!
The intricacies of tools with multiple load ports
For a more subtle example of where using an imperfect measure as a target can lead to suboptimalities when scheduling a wafer fab, we were inspired by a post on the excellent Factory Physics and Automation blog looking at the relationship between load port utilisation and cycle time. In our experience, we have seen load port utilisation of a tool used as a target when designing both operator workflows and dispatching rules.
First, some quick definitions. Many tools in a fab have multiple ‘load ports’ where lots can be inserted into the tool, but then a limited chamber capacity so that, for instance, only one wafer can be processed in the chamber at the same time.
Consider the machine in Fig. 1 with three chambers and two load ports. Lots can be loaded in either load port, but then each wafer in the lot has to move through Chambers A, B and C one at a time. This means wafers may have to queue inside the tool, if the next chamber they need is still processing. Lots must be unloaded at the same load port in which they were inserted. Suppose it takes each chamber 10 minutes to process a wafer, and we want to process two lots each consisting of three wafers. If we were only allowed to use a single load port, we would have to wait for the first lot to move through all three chambers and exit at the same load port before we can start processing the second lot. Fig.2 shows that for a simple model (that ignores transfer time between chambers), the second lot will have to wait 50 minutes before it can start processing.
If however, an operator loads both batches into the two load ports at the same time (Fig. 3), the machine will pick up the first wafer of the second lot as soon as the first lot has finished processing in chamber A. Thus the second lot will only need to wait 30 minutes.
Therefore, for a given level of WIP at a tool, we can expect higher load port utilisation to be correlated with reduced waiting and therefore improved cycle time.
Indeed, in cases where a wafer cannot be unloaded from a tool until all the wafers in the same lot are also ready to be unloaded (a common workflow), it can actually make sense to split lots before a chamber tool. For instance, if we have a lot of 6 wafers before the tool (see Fig. 1) – loading all the wafers as a single lot in a load port – it will take 80 minutes for all 6 wafers to move through the three chambers until we can unload the lot. If however, we split the original lot into two lots of three and load them into both load ports (as in Fig. 3), then the first lot can be unloaded after just 50 minutes, and potentially continue to its next step earlier.
How directly targeting load port utilisation can harm cycle time
As predicted by Goodhart’s Law, the correlation between load port utilisation and fab cycle time breaks down once we try to optimize directly for load port utilisation. This breakdown is particularly stark on photolithography tools, where process steps rely on a critical secondary resource: reticles. Reticles (also called photomasks) act like stencils in the expose step of a photolithography process, patterning the wafer with the desired features. In most photo tools, reticles must be loaded onto the tool in containers, called pods, before the lots that require them can be loaded onto the machine. Therefore, if a lot is inserted into a load port early, the wafers could just be waiting inside the machine. Moreover, this also requires loading a reticle into the machine when it could have a more productive use elsewhere.
For a simple example, consider a toolset consisting of two of the tools from Fig. 1 (we can imagine chambers A, B and C are performing coat, expose and develop operations respectively).
Suppose we have just loaded a 3 wafer lot onto tool 1. The other load port of tool 1 remains free. Meanwhile on tool 2, both load ports are utilised, but there are only two wafers yet to be processed in Chamber A.
A lot (lot X) that requires a special reticle (of which only one exists) arrives. Due to a lot-level restriction, lot X can only run on tool 1. This sort of restriction is particularly common in photolithography where running consecutive photo layers through the same tool (even if there are multiple tools qualified for the operation) can reduce product variability caused by idiosyncratic aspects of the lensing to a particular tool (this is sometimes known as a ‘lot-to lens’ dedication).The operators on this toolset abide by the following rule for dispatching lots:
Rule 1: If a load port and the required reticle are available, load the reticle and the lot onto the tool.
Since tool 1 has a load port available, the operator immediately loads the reticle onto the machine, and puts lot X into the load port.
Ten minutes later, lot Y arrives at the toolset, also requiring the same reticle, and with a lot-level restriction forcing it to run on tool 2. Since the reticle is already loaded on tool 1, lot Y cannot be dispatched until lot X has finished processing and the reticle has been moved from tool 1 to tool 2. Assume, for the purpose of simplicity, the reticle moves instantaneously, both lots will have finished processing in 130 minutes time (see Fig. 4).
Imagine, however, the operators adopted the following workflow:
Rule 2: If a load port and the required reticle are available and the tool can begin processing immediately (i.e. Chamber A is free), load the reticle and the lot onto the tool
In this case, lot X will not be immediately loaded onto tool 1, since Chamber A is initially occupied. After only 20 minutes though, lot Y can be loaded onto tool 2, to finish processing 50 minutes later, at which the reticle can be moved and lot X can start on tool 1. Thus, after just 120 minutes (as opposed to the 130 minutes under Rule 1), both lot X and lot Y will have finished processing. Therefore, we can see that by adopting rule 2, the cycle time, and hence the throughput of the toolset can be improved.
In our experience of wafer fabs, we often see workflows akin to Rule 1, wherein operators fill the load ports of photo tools as soon as they are free, thus forfeiting the opportunity to use reticles earlier on different tools. Adopting a workflow like Rule 2, however, is more difficult since it requires operators to have foreknowledge of when the tool will be ready to process a new lot, and reacting promptly to load the tool at precisely this time. In practice, particularly when operator availability is limited, you will risk increasing wait time because you leave the tool under utilised if you fail to load a lot as soon as a machine becomes available.
Using advanced optimization to handle Goodhart's Law
Flexciton’s scheduler can help to alleviate this problem by employing advanced optimization technology. It can predict when lots will arrive at the photo toolset and which reticles they will require, and then jointly schedule the reticles and lots on the toolset to obtain an optimized schedule. The knowledge of future arrivals crucially allows us to identify cases where loading a reticle onto a machine now is suboptimal, since a lot will soon arrive at another tool that can make use of the reticle sooner or that simply has a higher priority. Thus, following a Flexciton schedule, operators can dispatch to load ports when they become available, with minimal risk of harming cycle time due to locking in reticles prematurely.
However, we still are not immune to the curse of Goodhart’s Law. The cycle time of an optimized schedule is itself only a proxy for what we actually care about: producing more high quality wafers at a low cost per wafer. Over-optimizing for cycle time may lead to a solution with so many loads and unloads that the labour cost of running fab becomes prohibitive. Or, as described in one of our previous blog posts, the solution may require moving reticles so frequently between tools that we increase the chance of a costly breakage.
To solve this, we apply a technique suggested by Andy Grove himself: we use pairing indicators. Combining indicators, where one has an effect counter to the other, avoids the trap of optimizing one at the expense of another. This is why we typically pair cycle time with the number of batches (to account for limited operator availability) or the number of reticle moves (to keep the risk of reticle damage low), thus mitigating the perils of Goodhart’s Law.
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Staying ahead in smart manufacturing technologies has become paramount for global competitiveness. This topic was the focal point of the recent panel discussion webinar hosted by Flexciton.
The semiconductor industry's journey toward fully autonomous manufacturing is underway, driven by advanced technologies and strategic investment. Staying ahead in smart manufacturing technologies has become paramount for global competitiveness. This topic was the focal point of the recent panel discussion webinar, hosted by Jamie Potter, Flexction CEO & Cofounder. The panel featured industry leaders representing fabs and suppliers: Matthew Johnson, VP of Wafer Fab Operations at Seagate; Patrick Sorenson, Industrial Engineer at Microchip Technology; Francisco Lobo, CEO of Critical Manufacturing; and Madhav Kidambi, Technical Marketing Director at Applied Materials.
Survey Insights: Where Are We Now?
The panel discussion was initiated with a presentation of the findings from Flexciton's inaugural Front End Manufacturing Insights survey, conducted among fabs in the US, Europe, and Asia. Key takeaways included:
- A majority of respondents see autonomous manufacturing as achievable within the next decade.
- Data standardization and integration remain major barriers, delaying scalable solutions.
- Cloud computing, IoT and Mathematical Optimization stand as the top three advanced technologies that fabs have adopted so far.
These insights laid a strong foundation for a lively discussion, highlighting the shared vision while addressing divergent strategies and challenges.
Insights from Industry Experts
Pragmatism Over Perfection in Data Models
Francisco Lobo emphasized the importance of starting with what’s available when building scalable solutions.
“Instead of building a complete model from scratch, leverage existing standards and your MES infrastructure. Begin with a pragmatic approach and evolve as you learn.”
This iterative strategy ensures companies can start deriving value early, without waiting years for a perfect model to be developed.
Strategic Investments In Downturns
While many fabs postpone investments during downcycles, Matthew Johnson emphasizes that smart manufacturing investments should be continuous rather than cyclical. He highlighted the strategic advantage of such approach:
“In down cycles, you often need these solutions the most. For example, using smart manufacturing to scale metrology tools through sampling can significantly stretch your existing resources without capital-heavy investments.”
His insight underscores how downturns provide a window to refine processes for long-term gains.
Getting Leadership Buy-in
Securing leadership support for smart manufacturing investments remains challenging when benefits aren't immediately apparent. Patrick Sorenson shares that the ROI justification was easier during the recent upcycle:
"If we just get a few more lots out of the fab when we have more demand than capacity, that will pay for itself."
In other scenarios, focus on demonstrating benefits through yield improvements, capital avoidance, or labor efficiency.
Industry Alignment on the Vision
Madhav Kidambi observed a growing consensus around the end goal of autonomous manufacturing, even as companies differ in their pathways:
“The vision of Lights Out manufacturing is clear, but strategies are evolving as companies learn how to justify and sequence investments to sustain the journey.”
Ecosystem Collaboration and The Path Towards Autonomy
A key theme emerging from the discussion is the importance of collaboration between suppliers and fabs. This includes:
- Open platforms and integration capabilities
- Standardized data protocols
- Partner ecosystems for specialized solutions
- Shared innovation initiatives
As the industry progresses toward autonomous manufacturing, success will depend on:
- Maintaining continuous investment in smart technologies
- Taking pragmatic approaches to data integration
- Developing clear ROI frameworks
- Fostering collaboration across the ecosystem
- Building upon existing systems and standards
As Matt from Seagate concludes,
"Fab operation is really a journey of continuous improvement, and the pursuit of smart technologies is a fundamental tenet of our strategy to ensure that we meet the objectives as an organization."
Watch the Full Webinar
The conversation is packed with actionable insights on overcoming barriers, achieving quick wins, and navigating the complexities of smart manufacturing adoption. Don’t miss out—click here to watch the full discussion recording.
Innovate UK, part of UK Research and Innovation, has invested in Flexciton and Seagate Technology's production planning project to help improve UK semiconductor manufacturing.
London, UK – 1 Oct – Flexciton, a UK-based software company at the forefront of autonomous semiconductor manufacturing solutions, is excited to announce investment from Innovate UK in a strategic collaboration with Seagate Technology’s Northern Ireland facility. Innovate UK, the UK’s innovation agency, drives productivity and economic growth by supporting businesses to develop and realize the potential of new ideas. As part of their £11.5 million investment across 16 pioneering projects, this collaboration will help develop and demonstrate cutting-edge technology to boost semiconductor manufacturing efficiency and enhance the UK’s role in the global semiconductor supply chain.
Jamie Potter, CEO and Cofounder of Flexciton, commented:
"We are thrilled to partner with Seagate Technology to bring yet another Flexciton innovation to market. By combining our autonomous scheduling system with Flex Planner, we are enhancing productivity in semiconductor wafer facilities and driving greater adoption of autonomous manufacturing."
The partnership aligns directly with the UK government’s National Semiconductor Strategy, which seeks to secure the UK’s position as a key player in the global semiconductor industry. Flexciton’s contribution to this strategy is not just a testament to its cutting-edge technology but also highlights the company’s role in reinforcing supply chain resilience and scaling up manufacturing capabilities within the UK.
Flex Planner: A breakthrough solution for chip manufacturing
At the heart of this project is Flex Planner, the first closed-loop production planning solution for semiconductor manufacturing with the ability to control the flow of WIP in a fab over the next 2-4 weeks, autonomously avoiding dynamic bottlenecks, reducing cycle times, and improving on-time delivery performance.
Supporting the UK's semiconductor growth
The UK government’s investment in semiconductor innovation underlines its commitment to fostering cutting-edge solutions that bolster the sector’s growth. The semiconductor industry is projected to grow from £10 billion to £17 billion by 2030, with initiatives like this collaboration driving the innovation necessary to achieve these goals.
Flexciton’s partnership with Seagate exemplifies how collaboration between technology innovators and manufacturers can lead to transformative advances in the industry. The funding from Innovate UK enables both companies to develop and test solutions that not only enhance productivity but also position the UK as a critical link in the global semiconductor ecosystem.
About Flexciton
Flexciton is pioneering autonomous technology for production scheduling and planning in semiconductor manufacturing. Leveraging advanced AI and optimization technology, we tackle the increasing complexity of chipmaking processes. By simplifying and streamlining wafer fabrication with our next-generation solutions, we enable semiconductor fabs to significantly enhance efficiency, boost productivity, and reduce costs. Empowering manufacturers with unmatched precision and agility, Flexciton is revolutionizing wafer fabrication to meet the demands of modern semiconductor production.
For media inquiries, please contact: media@flexciton.com
The semiconductor industry is set to receive $1tn in investment over the next six years, driven by AI and advanced technologies, with over 100 new wafer fabs expected. However, labor shortages continue to pose a challenge, pushing the need for autonomous wafer fabs to ensure continued growth.
Over the next 6 years, the semiconductor industry is set to receive around $1tn in investment. The opportunities for growth – driven by the rapid rise of AI, autonomous and electric vehicles, and high-performance computing – are enormous. To support this anticipated growth, over 100 new wafer fabs are expected to emerge worldwide in the coming years (Ajit Manocha, SEMI 2024).
However, a significant challenge looms: labor. In the US, one-third of semiconductor workers are now aged 55 or older. Younger generations are increasingly drawn to giants like Google, Apple and Meta for their exciting technological innovation and brand prestige, making it difficult for semiconductor employers to compete. In recent years, the likelihood of employees leaving their jobs in the semiconductor sector has risen by 13% (McKinsey, 2024).
To operate these new fabs effectively, the industry must find a solution. The Autonomous Wafer Fab, a self-optimizing facility with minimal human intervention and seamless production, is looking increasingly likely to be the solution chipmakers need. This vision, long held by the industry, now needs to be accelerated due to current labor pressures.
Thankfully, rapid advancements in artificial intelligence (AI) and Internet of Things (IoT) mean that the Autonomous Wafer Fab is no longer a distant dream but an attainable goal. In this blog, we will explore what an Autonomous Wafer Fab will look like, how we can achieve this milestone, the expected outcomes, and the timeline for reaching this transformative state.
What will an Autonomous Wafer Fab look like?
Imagine a wafer fab where the entire production process is seamlessly interconnected and self-regulating, free to make decisions on its own. In this autonomous environment, advanced algorithms, IoT, AI and optimization technologies work in harmony to optimize every aspect of the manufacturing process. From daily manufacturing decisions to product quality control and fault prediction, every step is meticulously coordinated without the need for human intervention.
Key features of an Autonomous Wafer Fab:
Intelligent Scheduling and Planning: The heart of the autonomous fab lies in its scheduling and planning capabilities. By leveraging advancements such as Autonomous Scheduling Technology (AST), the fab has the power to exhaustively evaluate billions of potential scenarios and guarantee the optimal course for production. This ensures that all constraints and variables are considered, leading to superior outcomes in terms of throughput, cycle time, and on-time delivery.
Real-Time Adaptability: An autonomous fab is equipped with sensors and IoT devices that continuously monitor the production environment. These devices can feed real-time data into the scheduling system, allowing it to dynamically adjust schedules and production plans in response to any changes or disruptions.
Digital Twin: Digital Twin technology mirrors real-time operations through storing masses of data from sensors and IoT devices. This standardized data schema allows for rapid introduction of new technologies and better scalability. Moreover, by simulating production processes, it helps to model possible scenarios – such as KPI adjustments – within the specific constraints of the fab.
Predictive maintenance: Predictive maintenance systems will anticipate equipment failures before they occur, reducing downtime and extending the lifespan of critical machinery. This proactive approach ensures that the fab operates at peak efficiency with minimal interruptions. Robotics will carry out the physical maintenance tasks identified by these systems, and when human intervention is necessary, remote maintenance capabilities will allow technicians to diagnose and address issues without being on-site.
The Control Room: In an autonomous fab, decision-making is driven by data and algorithms. The interconnected system can balance trade-offs between competing objectives, such as maximizing throughput while minimizing cycle time, with unparalleled precision. That said, critical decisions such as overall fab objectives may still be left to humans in the “control room”, who could be on the fab site or 9000 km away…
How can we get there?
Achieving the vision of an Autonomous Wafer Fab requires a multi-faceted approach that integrates technological innovation, strategic investments, and a cultural shift towards embracing automation. Here are the key steps to pave the way:
A Robust Roadmap: All fabs within an organization need to have a common vision. Key milestones need to be laid out to help navigate each fab through the transition with clear actions at each stage. SEMI’s smart manufacturing roadmap offers an insight into what this could look like.
Investing in Novel Technologies: The pivotal step towards autonomy is investing in the latest technologies, including AI, machine learning, AST, and IoT. These technologies form the backbone of the autonomous fab, enabling intelligent planning and scheduling, real-time monitoring, and adaptive control.
Data Integration and Analytics: A crucial aspect of autonomy is the seamless integration of data from various sources within the fab. By harnessing big data analytics, fabs can not only gain deep insights into their operations, but they will have the correct data in place to support autonomous systems further down the line.
Developing Skilled Workforce: While the goal is to minimize human intervention, the semiconductor industry will still require skilled professionals who can manage and maintain advanced systems. Investing in workforce training and development to fill the current void is essential to ensure a smooth transition.
Collaborative Ecosystem: Even the biggest of chipmakers is unlikely to reach the autonomous fab all on their own. Collaboration with technology providers, research institutions, and industry partners will be key. Sharing knowledge and best practices can accelerate the development and deployment of autonomous solutions.
Pilot Programs and Gradual Implementation: Transitioning to an autonomous fab should be approached incrementally. Starting with pilot programs to test and refine technologies in a controlled environment will help identify challenges and demonstrate the benefits. Gradual implementation allows for continuous improvement and adaptation.
How will fabs benefit?
The transition to an Autonomous Wafer Fab promises a multitude of benefits that will revolutionize semiconductor manufacturing:
Enhanced Efficiency: By optimizing production schedules and processes, autonomous fabs will achieve higher throughput and better resource utilization. This translates to increased production capacity and reduced operational costs.
Better Quality: Advanced process control and real-time adaptability ensure consistent product quality, minimizing defects and rework. This leads to higher yields and greater customer satisfaction.
Reduced Downtime: Predictive maintenance and automated decision-making reduce equipment failures and production interruptions. This results in higher uptime and more reliable operations.
Improved Flexibility: Autonomous fabs can quickly adapt to changing market demands and production requirements. This flexibility enables manufacturers to respond rapidly to customer needs and stay competitive in a dynamic industry.
Cost Savings: The efficiencies gained from autonomous operations lead to significant cost savings. Reduced labor intensity, lower material waste, and optimized energy consumption contribute to a more cost-effective production process.
Sounds great, but when will it become a reality?
The journey towards an Autonomous Wafer Fab is well underway, but the timeline for full realization varies depending on several factors, including technological advancements, industry adoption, and investment levels. However, significant progress is expected within the next decade.
Short-Term (1-3 Years):
- Implementation of pilot programs and continual adoption of AI, IoT, AST and other advanced technologies.
- Incremental improvements in scheduling, process control, and maintenance practices.
Medium-Term (3-7 Years):
- Broader adoption of autonomous solutions across the industry.
- Enhanced data integration and analytics capabilities.
- Development of a skilled workforce to support autonomous operations.
Long-Term (7-10 Years and Beyond):
- Full realization of the Autonomous Wafer Fab with minimal human intervention.
- Industry-wide standards and best practices for autonomous manufacturing.
- Continuous innovation and refinement of autonomous technologies.
Conclusion
The pathway to the Autonomous Wafer Fab is a transformative journey that holds immense potential for the semiconductor industry. By embracing advanced technologies, fostering collaboration, and investing in the future workforce, fabs can unlock unprecedented levels of efficiency, quality, and flexibility. Autonomous Scheduling Technology, as a key pillar, will play a crucial role in this evolution, driving the industry towards a future where production is seamless, self-optimizing, and truly autonomous. The vision of an Autonomous Wafer Fab is not just a distant possibility but an imminent reality, poised to redefine the landscape of semiconductor manufacturing.
Now available to download: our new Autonomous Scheduling Technology White Paper
We have just released a new White Paper on Autonomous Scheduling Technology (AST) with insights into the latest advancements and benefits.
Click here to read it.