Building and maintaining any form of scheduling solution to be flexible yet robust is not an easy undertaking. Commonly, fab managers have resorted to rule-based dispatch systems or other discrete-event simulation software to estimate how their fab will play out in the near future. Often this requires deciding a specific KPI that is important to the fab up-front; do I care more about getting wafers out the door, or reducing the cycle time of those wafers?

Competing objectives challenge

As a fab manager, there are a number of competing objectives to balance on the shop floor that all impact the profitability of the fab. Whether that be reliably delivering to customers their contractual quantities on time, or ensuring that fab research and development iteration time is kept low, fabs need a flexible, configurable scheduling solution that can produce a variety of schedules which account for these tradeoffs. At Flexciton, we call this “multi-objective” scheduling; optimizing the factory plan whilst considering several independent KPIs that, in this case, are fundamentally at odds with one another. This article explores Flexciton’s approach to multi-objective scheduling and how we expose simple configurations to the fab manager, whilst allowing our scheduling engine to ultimately decide on how that configuration plays out in the fab.

If there is no automated real-time dispatch system in the fab, determining the "best" schedule is a very complex procedure that cannot even be accomplished with advanced spreadsheet models. Assuming that the fab is advanced enough such that a dispatch system is in place, it will likely only consider "local" decisions pertaining to the lots that are immediately available to the dispatch system at the time the decision is made.

Dispatch systems typically do not have the configurability to adjust the user's incremental utility with respect to throughput and cycle time; they typically adhere to a series or hierarchy of rules that are tuned to consider exactly one KPI. Therefore to change the objective of the dispatch system would require rewriting these rules; an often time-consuming exercise that requires advanced technical knowledge of the dispatch system. This makes it almost impossible or otherwise very time consuming to trial various configurations of the fab manager’s preferences.

Balancing various objectives for best results

The Flexciton optimization engine is a multi-objective solution that can linearly balance various KPIs according to user-chosen weights. As these weights are exposed to the end-user, this renders the possibility of running many different scenarios with varying preferences trivial. Fab managers can have access to the specific weight values themselves or work with our expert optimization engineers to select from a handful of high-level configurations and the solution will select appropriate weights itself.

To properly understand the flexibility of the engine, we will now step through four case studies. The goal is to compare how, given the same dataset, slightly different objective configurations impact the solution that is returned by accounting for the change in preferences.

We present a schedule of nine tools from across five toolsets with seventy lots of a mix of 65% Priority1 lots. Each lot can go to a random subset of tools within a single toolset.

The schedule will then be tested against four runs:

1. Produced by a dispatch system with heuristic rules
2. Optimized for cycle time
3. Optimized for the on-time delivery of wafers
4. Balanced optimization considering both cycle time and OTD

For each of these scenarios, we will present two gantt charts; one labelled with the “Queueing Time” of each lot (aka “rack time”) and another labelled with the “Late Time” of each lot. Late time refers to the duration by which the lot completed processing after its due date. If it was not late, the label reads “0s” since we do not consider being more early as being more favourable. Lots that are considered high priority (Priority 1 to 3) are given a circle badge indicating such. Low priority lots are Priority 4 through 10. Each lot is coloured according to this priority class.

Case study #1: base case - greedy dispatch

To begin, we’ll present how a schedule could look when produced by a dispatch heuristic that does not consider the future arrivals of wafers, but simply what is currently available in front of a tool. The greedy rule here is to just dispatch the highest priority wafer on the rack at the point the tool is idle.

In the above example, the high-priority wafers have to wait due to the system only considering what’s on the rack and therefore dispatching the low-priority wafers that are ready to go.

It should be noted that such a strategy is great for improving overall throughput and cycle time since the machine idle time is reduced by constantly dispatching wafers. This has the side effect of delivering all-bar-one of the wafers on time. In reality though, not all lots are equal and fab managers care a great deal more about certain high-priority lots thus making the scheduling problem quite a bit trickier.

Unfortunately, in order to reconfigure the system to place greater importance upon the high-priority wafers and dispatch them first would require complex rewriting of the dispatch  rules to “look ahead” at the wafers that are not yet on the rack, and are arriving shortly. The dispatcher would then elect to keep the machine idle in order to reduce the high-priority wafer cycle time.

Case study #2: Optimize for high-priority-lot cycle time

Instead of modifying the RTD rules, we can emulate what that would look like by running our optimization engine whilst optimizing for the cycle time of high-priority lots:

The low priority lots at the front of the schedule are replaced with high-priority lots so that they can be dispatched as soon as they arrive. These low priority lots have been pushed to the back of the schedule with non-zero rack time (since the cycle time of high priority lots matters so much more). Naturally this is at the cost of overall average cycle time which has suffered by 23% in order to improve Priority1 cycle time by 11%. Also note that on tool “SBXF/115”, our scheduling solution has pushed the Priority2 (orange) and the Priority10 (green) lots later so that the Priority1 (red) lots are rushed through with zero rack time.

Case study #3: Optimize for on-time delivery

With optimisation, there are no additional changes required to increase the flexibility of the system. We simply describe what a good schedule looks like using the multi-objective function and the optimizer does the rest. Subtle tweaks to this function will inevitably produce very different schedules. Now let’s take a look at how the schedule alters when we want to maximise solely on-time delivery.

As expected, cycle time is quite a bit worse than previously however now there are no lots delivered late. This is very similar to the original schedule produced by simple dispatch rules. The low-priority lots have been brought forward so that they are delivered on time and the cycle time of the high-priority lots suffer as a result.

Case study #4: Optimize for both

Finally, the main purpose of this article is to illustrate the ease of considering both KPIs with some relative weight simultaneously.

Note that the KPIs of cycle time and throughput are slightly worse than when that was the sole KPI being optimised. The key is that both are better than when the other KPI was being optimized. This balance is entirely in the hands of the fab manager. We maintain roughly the same cycle time of high-priority lots as when optimising for cycle time and fewer lots are late than when optimizing only cycle time.

Summary and Conclusions

This article has provided a number of ways that illustrate how optimization can be considered both more flexible and robust than heuristics that cannot effectively search the global solution space.

The engine is simple to tune due to the exposed weights and/or configurations presented to the fab manager which allow a high degree of customisation both with respect to the objective function and wafer priorities. This flexibility allows us to easily consider complex hierarchical objectives found in semiconductor manufacturing such as “optimise high-priority cycle time as long as no P1-8 lots are late” or “optimise batching efficiency (perhaps due to operator constraints) and then high-priority cycle time”. Ultimately, our solution is a market-leading scheduler that will realise true KPI improvements on your live wafer fabrication data.

Flexciton is currently offering the Fab Scheduling Audit free of charge. To enquire, please click here.

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Flexciton celebrated its fifth anniversary on May 17th. The past 12 months have been intensely positive, bringing new exciting projects and allowing the company to accelerate its growth. We took this opportunity and asked Flexciton's cofounders to reflect on their journey by answering the following five questions, independently.

Where did you expect Flexciton to be in 5 years when you started it 5 years ago?

Jamie Potter, CEO & Cofounder: Not where it is today. I think when we set out 5 years ago, we knew we had a cutting-edge technology but we weren't sure where the best application was for it. The semiconductor industry is the most complex manufacturing process in existence and it needs sophisticated technology to solve its problems. The average consumer would have never guessed how incredibly difficult it is to manufacture the semiconductors which make up all of the electronics they use today. We were lucky that 12 months after founding the company we found such a great partner, Seagate Technologies, who we have partnered with to bring this technology to Semiconductors.  

Dennis Xenos, CTO & Cofounder: I'd be lying if I said we had a crystal-clear vision of where we want the company to go in the first five years. However, we were well aware that we were developing a highly complex technology to address a particularly difficult manufacturing problem. We knew that it would take some time to see the results. Today, I am both grateful and excited that we now have a fully automated closed loop scheduling solution that runs the scheduling of the world's most complex manufacturing processes 24 hours a day, seven days a week, after five years! This would not have been possible without an incredible team of exceptional engineers and researchers who believed in and remained committed to the Flexciton vision to transform manufacturing from the start!

What would you do differently if you could go back in time?

Jamie: I would have built the solution from day one, the way it is now. It has taken us many years to develop our technology into the fully functional product that it is today. Along that journey, we attempted numerous things that didn't work. At the same time, this is to be expected; after all, that is the nature of R&D! I'm just incredibly proud that our team was able to bring this disruptive technology to market after more than ten years of academic research and five years of commercialisation.

Dennis: I wouldn't change a thing; it's been an incredible journey so far. I believe that where we are now is the result of all of the decisions, actions, and lessons we have learned along the way.

What’s been most surprising about the journey so far?

Jamie: The complexity of the problem we solve is so much more complex than we first imagined. The reality is that we set out to bring technology that had only been written about in academic papers through to industry. We realised quickly how there was still such a big gap between academia and the real world. This is why it has taken years of work by an incredibly talented team to bring this through to market.

Dennis: That Jamie and I had not lost any of the excitement and determination we had when we first started the company. The difficult times have toughened us, and the good times have strengthened our confidence that what we do is not just what we believe in but it's something the industry needs.

Flexciton smart technology is versatile and can be used to plan production in any manufacturing industry. Why did you decide to specialise solely in semiconductor wafer fabrication?

Jamie: The complexity of wafer fabrication is just staggering. In most factories you put raw materials through a few machines and the end product is created. In semiconductor wafer fabrication, you may need to put the raw material through 1000 machines. The complexity of the manufacturing process is exponentially higher than you find in almost any industry. What we do here at Flexciton is the state-of-the-art and the most complex manufacturing industry in existence is the best application of such technology.

Dennis: Semiconductor manufacturing is one of the most exciting industries.  Making the end product is hard; it involves thousands of steps and equally many resources. The wafer fab is a fast-paced environment, and over time, the manufacturing process becomes more complex, introducing more complicated operational constraints. Compared to other manufacturing types with even hundreds of production lines, our optimization-based scheduling technology can add significantly more value to the semiconductor industry. Furthermore, the advancement of manufacturing automation in wafer fabs makes the implementation and execution of our technology a much better fit.

What is your vision for the next 5 years?

Jamie: Our vision for the next 5 years is to bring Flexciton technology to semiconductor wafer fabs across the world to enable them to manufacture the next generation of products which make up the basis of all electronics. The complexity of semiconductor manufacturing is increasing all the time and many manufacturers struggle to manufacture the latest products at scale. We exist to solve that problem.

Dennis: In the next five years, I want us to bring Flexciton technology to as many wafer fabs as possible. Our technology provides a solution to the industry's existing problem, allowing fabs to increase throughput from existing equipment, efficiently manage production cycle time to ensure orders are delivered on time. I believe, Flexciton will be the ultimate scheduling technology to enable semiconductor fabs to run their production at a new efficiency level.

If you would like to ask Jamie or Dennis a question of your own, just use our contact form.

Batch tools are purposefully built to process two or more lots in parallel. However, due to the complexity and volatility of the wafer fabrication environment, each day, wafer fabs are challenged to make complicated batching decisions. How to determine when to batch lots together and when better not? This is what we shall call the ‘batch or not to batch dilemma’.   

Why batch lots together in a wafer fab?

One of the most precious commodities is time, and batch tools are designed to make the best use out of time. Often, these tools have a long processing time. An example would be a diffusion furnace. Instead of waiting for 6 or 8 hours for a single lot to go through the whole process before loading the next one, batching allows the processing of multiple lots simultaneously. This sounds like an easy way to solve a complex problem. However, with batch tools, on top of a whole host of constraints to respect, there are exponentially more scheduling options available compared to a non-batch tool.

In addition, the processing times of batch tools introduce a very complex dynamic. Batch tool processing times consist of fixed time (regardless of the number of lots in a batch) and a variable time (increases with each additional lot). Because of the fixed time component (+ tool setup time) in creating a batch, there is the perception that larger batch sizes are more efficient.

A typical approach to batching decisions

At a batch tool, there are a number of decisions to be made, such as whether you process lots that are already in front of the tool or wait for more to arrive? Additionally, if the number of lots waiting exceeds the tool capacity, which lots should you batch together and process first?  

Typically, each fab decides on their batch-size policy, which will guide the batching decisions. One of the commonly used policies would be a Minimum Batch Size (MBS) policy, setting a minimum number of lots required to start processing. This could be determined by running a large number of different simulations. These simulations will determine the batch size that provides the best performance for that specific use case. Thus, an MBS heuristic rule would be created, setting a certain direction to follow for all future lots.

On the other hand, a ‘near-full’ policy would require you to wait until the batch size is as close as possible to the maximum capacity of a tool. In this case, it is possible to achieve high throughput, but it can also cause the tool to stay idle for a long time when waiting for more lots to arrive in order to satisfy the policy, which negatively impacts on overall cycle time.

Processing a batch whenever the tool is available and ready to process, is another approach (this is called the ‘greedy policy’). This may reduce cycle time during times of low WIP, but will likely cause an increase to cycle time and lower throughput during times of high WIP.

How to determine the right batch size

The fact of the matter is that there isn’t one perfect batch size. In reality, it depends on the context of the whole system at that specific point in time. The batching decision relies on a number of dynamic factors:

• Max batch size constraints
• What will arrive and when
• What is currently in the queue
• Possible recipe combinations that can be batched together
• What priorities the wafers have
• What objective you are currently optimizing for

In many fabs, daily batching decisions are guided by a dispatch system which uses rules-based heuristic algorithms. This approach can work very well in some cases but can bring very poor results in others.

Let’s take a look at an example, where we use a simplistic approach to illustrate the problem.

Suppose we have a batch tool with a maximum batch size of 4 lots. In order to get better efficiency, a typical dispatch rule is to set a minimum batch size e.g. minimum batch size of 2 lots. However, in a situation where one lot is already present, waiting too long, will be inefficient. Therefore, a maximum wait time - let’s say 30 min - would apply to the rule of minimum batch size. If we have waited 30 min, and another lot did not arrive, the dispatch system would send one lot for processing.

Sometimes this works well - here another lot arrived within 5 minutes, allowing  2 lots to be processed simultaneously.

In this example, the rule was effective and achieved a lower average cycle time than if we hadn’t waited for the second lot to arrive. However, sometimes a rules-based dispatch system can make poor decisions. Typically the dispatch system will only make local decisions, and won’t look ahead to anticipate which lots and when are coming, as it’s illustrated in the second scenario below. The second lot arrives at the tool in 60 minutes, but due to the 30 min waiting rule, the first lot has already been dispatched.

In order to make more optimal decisions around batch sizes, we need to be able to anticipate what WIP will arrive, when it will arrive, and where this WIP goes next after the current batching step.  This can be achieved by applying a smart scheduling approach which understands the broader context required to make optimized batching decisions. The below example illustrates a rules-based decision vs an optimization approach.

This is one very simple example which shows some of the trade-offs that must be considered when making batch decisions. In the above case, it would be possible to write an extension to the dispatch rules to account for the scenario presented. However, in reality, there are several other factors which bring additional complications. For example, often, Lots will have different priorities. When a high priority lot is batched together with a lower priority lot, the average cycle time for both lots may be reduced compared to running the Lots in two sequential batches. That said, the cycle time of the high priority lot is likely to be increased - this may be undesirable.

Given how dynamic a fab is, writing dispatch rules to efficiently deal with the full range of scenarios is possible, but would be extremely time-consuming to maintain and expensive to build.

Conclusion

Batch tools are extremely complex machines to schedule, as there is a huge number of scheduling options, and each option has a different efficiency. Commonly used dispatch rules can cause poor performance in a dynamic fab environment. Often, the batching methodology follows a fixed rule, such as maximum batch size. These fixed rules can provide occasional good outcomes, but they are unable to consistently provide good solutions. As a result, the KPIs across the batch toolset might show undesirable increases in cycle time, or reductions in throughput if the WIP mix or objectives change. Additionally, creating very efficient rules would require a lot of time and extensive maintenance.

Smart scheduling, on the other hand, introduces the ability to make optimized batching decisions in any situation to achieve the objective of increased throughout or lower cycle times. By applying mathematical hybrid-optimization techniques, we are able to find a solution which is near-global-optimal, delivering a consistently high-quality outcome.

Get your Wafer Fab Scheduling Performance Analysis fully remotely and free of charge. Click here to get in touch.

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In our previous blog, we talked about The Theory of Constraints. One of the principles being, the system is only as good as its weakest module - i.e. a bottlenecked tool or toolset.

Because of bottlenecked toolsets, wafers will spend a great proportion of their cycle time queuing (non value-adding) rather than processing. The longer or more uncertain the wait time, the higher risk of variability in the cycle time. This ultimately impacts the overall productivity of a fab. ‍

The wait time challenge

The Pareto Principle (aka the 80/20 rule) implies that most things in life are not distributed evenly. The very same rule can apply to wait time vs processing time in fabs. We have carried out performance analyses of numerous fabs recently, and we have witnessed the Pareto Principle repeatedly across various wafer fabs.

It is obvious that in order to maintain high performance in a fab, wafers are required to be processed within a predictable time frame and in doing so, spend as little time as possible queuing. However, it is very common to observe a significant disproportion within cycle time, where the wait time is significantly longer to processing time. In some cases, we have seen wait/process time ratios of 70% to 30%, respectively.

If we can reduce the queue time, we can reduce cycle time. By drilling down the wait time, we repeatedly observed the Pareto Principle highlighting that ~20% of toolsets contribute to ~80% of all wait time.

The bottleneck tool is not always the root cause

In order to solve the wait time problem, we first need to identify where in the fab the wafers spend the most time waiting to be processed. Typically, the culprit toolsets are those which deal with wafer re-entry and possess challenging constraints that heighten the scheduling difficulty, such as photo, EPI and Implant.

Improving the sequencing or mix of WIP directly at the bottlenecked toolsets seems to be the obvious starting point. However, whilst the bottleneck toolsets are incurring the most amount of wait time, they are not always the root cause of the problem. This may occur elsewhere.

For example, if the toolset is being fed WIP in which the associated recipe is unavailable for the next few hours, then this WIP has no option other than to wait. The toolset becomes bottlenecked, however, the root cause was ineffective WIP management of toolsets upstream.

Optimizing WIP to reduce wait times

In order to reduce wait times at the bottleneck areas, we must control and optimize the WIP flow. This can be achieved by applying smart scheduling, which advises dispatch on the best real time decisions to take.

Smart scheduling can first be applied to the bottleneck toolset in question to drive local efficiencies that relate to the specific constraints and cost functions of that toolset. For example, you can use optimization to:

• Optimize batching decisions (the wait or not to wait dilemma)
• Optimize changeover sequencing
• Balance the toolset loading
  

By optimizing at a local level, you can achieve increases in efficiency, which leads to cycle time reduction. However, the scheduling is still at the mercy of which WIP is sent from upstream toolsets. The next step is to schedule toolsets globally (scheduling multiple toolsets together) so that the WIP mix arriving from upstream toolsets is optimally sequenced to facilitate the heavily loaded bottleneck.

Referring to the example in the previous chapter, by applying advanced global scheduling, the upstream toolset can be scheduled to prioritize WIP such that it matches the available recipe of the downstream toolset. As a result, the wait time at the bottleneck toolset can be reduced even further than if you had just optimized the toolset locally. Additionally, by having a better future forecast on where WIP will be, you can better optimize load port conflicts (IPP conflicts) and auxiliary resources (reticles at photo tools).

At Flexciton, our optimization strategy is to:

• Optimize local toolsets which incur most cycle time
• Expand to global scheduling to balance the entire system

Identifying the problem

By taking a global view of a fab, we can analyse the cycle time across the whole fab and begin to determine where to focus optimization efforts in order to improve cycle time KPIs.

At Flexciton, we have developed an analytical tool (Wafer Fab Performance Analysis), which uses historical MES data to provide a comprehensive view of fab performance. Using the tool, we are able to locate bottleneck toolsets across the fab and quantify the impact each toolset has on cycle time. Through this global analysis, we are able to recommend an optimization strategy outlining where cycle time improvements can be made.

Although fab managers are acutely aware of the overall efficiency of cycle time at their fab, employing the Wafer Fab Performance Analysis allowed us to accurately quantify the problem and pinpoint where efficiencies are lost.

Click here to get in touch and learn more about complimentary Wafer Fab Performance Analysis for your fab.

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Any manageable system is limited by at least one constraint. What if this system is the most complex manufacturing process in existence? Providing optimized production scheduling for wafer fabs, we deal with a great number of constraints.

In the early 80s, Eli Goldratt, a physicist turned management guru published his book "The Goal". Why is it important? In the book, he underlined the foundations for what was described later as the "Theory of Constraints".  

Although it was written nearly 40 years ago, and manufacturing industries have since evolved, many of the principles stated in the book remain relevant today. They also mirror what we do at Flexciton, helping wafer fabs to improve performance by optimizing the way the fabrication process is scheduled.

Let us give you a snippet of what is it about by sharing the three key messages of the Theory of Constraints:

• "Throughput is the money coming in." Balancing the flow of product through the plant with demand from the market is critical for the business performance. Throughput is usually a priority KPI we follow to calibrate our scheduling solution for a given fab.  
• "Every hour lost at a bottleneck is an hour lost in the entire system." Based on the Wafer Fab Performance Analysis which we have carried out for various fabs, we see that a small number of tools are bottlenecks, yet this seemingly little number are responsible for substantial wait time in the fab.
• "We shouldn't be looking at each local area and trying to trim it. We should be trying to optimize the whole system. A system of local optimums is not an optimum system at all." At Flexciton, we first take a global look across the whole fab, then we identify areas where optimization would bring the most significant improvement for the entire facility.

The very first step for improving the fab productivity and KPIs such as throughput or cycle time is to identify the system's constraints and where the efficiency is lost. Flexction's Wafer Fab Performance Analysis takes a global view of a fab and pinpoints the areas or specific toolsets responsible for lower productivity. This analysis often brings unexpected and eye-opening results.

We are currently running the Wafer Fab Performance Analysis fully remotely and free of charge. If you wish to discover more, please click here to get in touch with one of our consultants.

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A consistent metric that wafer fab and foundry managers seek is high throughput. Being able to control and maximise throughput is critically important to the health and profitability of a semiconductor business. If the factory in question is capacity constrained, then any percentage increase to total fab throughput can be converted into further revenue for the business. Higher throughput can also prevent the need to invest in additional expensive CAPEX.

Achieving operational excellence in semiconductor manufacturing is a very difficult task and requires sophisticated industrial engineering for the best results. Fabs are highly dynamic and can be unpredictable at the best of times (tool downtimes and changing priorities), thus sustaining consistently high throughput is a major challenge for any fab manager or industrial engineer.

In this article, we are going to outline two factors that are key culprits in limiting semiconductor fab throughput, and how to control them by applying smart scheduling strategy.

Factor #1 - WIP mix at bottleneck toolsets

Wafer fabs have to deal with managing bottleneck toolsets since tools can be expensive and some deal with reentrant flow. Bottlenecks have a negative effect on cycle time and overall throughput, however, it is how we manage the sequencing and flow of WIP through these bottlenecks that leads to a productive and well-balanced factory.

At Flexciton, we repeatedly see Pareto's principle reveal that 20% of toolsets are responsible for 80% of wait time in the fab. Tackling the queue time first behind these key toolsets can significantly help with maximising global throughput across the fab, and should be on the priority list for industrial engineers.

Production progresses as quickly as the worst bottleneck allows. An hour lost at a bottleneck is an hour lost for the entire system (following the theory of constraints).

When a toolset is a fixed bottleneck (always in high demand), then it should be a priority to increase throughput at this step as the first point of call. Thus, the rest of the fab benefits from a higher overall throughput as a result. This can be achieved by improving load balancing across the toolset and better scheduling of upstream WIP to ensure optimum sequencing. For more complicated toolsets, you may need to consider optimizing changeovers or consider the scheduling of auxiliary resources (reticles for photo) in order to provide relief to the bottleneck.

It is also common to witness dynamic bottlenecking, whereby the bottleneck toolset may change over time as a result of the changing factory dynamics. For example, WIP might suddenly inflate at implantation toolsets from time to time, but will not always have a consistently high level of WIP behind it. Here, managing the global flow of WIP between toolsets (upstream and downstream) helps to alleviate these dynamic cases of WIP buildup, leading us on to the second discussion point below.

Factor #2 - Local optimization of toolsets

It is common to measure fab performance using local KPIs and targets, broken down by area or toolset for the operators. Thus, there is pressure to maintain constant high throughput across all toolsets (with little or no consideration of the impact this has on upstream or downstream tools).

Unfortunately, when local optimization occurs within toolsets, it can result in a significant imbalance to the overall fab system - leading to poor throughput. Put simply, if we process a load of WIP through 'toolset A' really quickly, then this may end up sitting at 'toolset B' for hours because the recipes are not available! Going back to point 1, this is highly inefficient and is likely to inflate bottlenecks and grind away at your throughput potential!

A wafer fab is a complex, intricate system, and we must optimize the flow across all toolsets to ensure high overall throughput. A fab in which all tools are working at max capacity at all times is actually very inefficient, and a system of local optimums is not an optimum system at all.

We want to ensure that what is being produced upstream of bottleneck toolsets are fed with optimum WIP mixture. Doing this can provide exponential increases in a fab overall system efficiency and ultimate throughput!

Smart scheduling, and its impact on throughput

The key to managing WIP flow across the entire fab system is to introduce high-quality scheduling into the process. By being able to comprehend upstream and downstream WIP flow, we begin to schedule globally across toolsets (rather than locally), and this is the key to minimising the severity of dynamic bottlenecks.

Smart scheduling is critical, as it allows the fab manager to balance priorities and make smart decisions in advance that aid greater throughput. By feeding directly to the dispatch system or providing operators with clear, specific direction on the next actions, it improves predictability and performance throughout the fab.

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