Tackling Variability With AI-based Process Control

Jon Herlocker, co-founder and CEO of Tignis, sat down with Semiconductor Engineering to talk about how AI in advanced process control reduces equipment variability and corrects for process drift. What follows are excerpts of that conversation.

SE: How is AI being used in semiconductor manufacturing and what will the impact be?

Herlocker: AI is going to create a completely different factory. The real change is going to happen when AI gets integrated, from the design side all the way through the manufacturing side. We are just starting to see the beginnings of this integration right now. One of the biggest challenges in the semiconductor industry is it can take years from the time an engineer designs a new device to that device reaching high-volume production. Machine learning is going to cut that to half, or even a quarter. The AI technology that Tignis offers today accelerates that very last step — high-volume manufacturing. Our customers want to know how to tune their tools so that every time they process a wafer the process is in control. Traditionally, device makers get the hardware that meets their specifications from the equipment manufacturer, and then the fab team gets their process recipes working. Depending on the size of the fab, they try to physically replicate that process in a ‘copy exact’ manner, which can take a lot of time and effort. But now device makers can use machine learning (ML) models to autonomously compensate for the differences in equipment variation to produce the exact same outcome, but with significantly less effort by process engineers and equipment technicians.

SE: How is this typically done?

Herlocker: A classic APC system on the floor today might model three input parameters using linear models. But if you need to model 20 or 30 parameters, these linear models don’t work very well. With AI controllers and non-linear models, customers can ingest all of their rich sensor data that shows what is happening in the chamber, and optimally modulate the recipe settings to ensure that the outcome is on-target. AI tools such as our PAICe Maker solution can control any complex process with a greater degree of precision.

SE: So, the adjustments AI process control software makes is to tweak inputs to provide consistent outputs?

Herlocker: Yes, I preach this all the time. By letting AI automate the tasks that were traditionally very manual and time-consuming, engineers and technicians in the fab can remove a lot of the manual precision tasks they needed to do to control their equipment, significantly reducing module operating costs. AI algorithms also can help identify integration issues — interacting effects between tools that are causing variability. We look at process control from two angles. Software can autonomously control the tool by modulating the recipe parameters in response to sensor readings and metrology. But your autonomous control cannot control the process if your equipment is not doing what it is supposed to do, so we developed a separate AI learning platform that ensures equipment is performing to specification. It brings together all the different data silos across the fab – the FDC trace data, metrology data, test data, equipment data, and maintenance data. The aggregation of all that data is critical to understanding the causes of a variation in equipment. This is where ML algorithms can automatically sift through massive amount of data to help process engineers and data scientists determine what parameters are most influencing their process outcomes.

SE: Which process tools benefit the most from AI modeling of advanced process control?

Herlocker: We see the most interest in thin film deposition tools. The physics involved in plasma etching and plasma-enhanced CVD are non-linear processes. That is why you can get much better control with ML modeling. You also can model how the process and equipment evolves over time. For example, every time you run a batch through the PECVD chamber you get some amount of material accumulation on the chamber walls, and that changes the physics and chemistry of the process. AI can build a predictive model of that chamber. In addition to reacting to what it sees in the chamber, it also can predict what the chamber is going to look like for the next run, and now the ML model can tweak the input parameters before you even see the feedback.

SE: How do engineers react to the idea that the AI will be shifting the tool recipe?

Herlocker: That is a good question. Depending on the customer, they have different levels of comfort about how frequently things should change, and how much human oversight there needs to be for that change. We have seen everything from, ‘Just make a recommendation and one of our engineers will decide whether or not to accept that recommendation,’ to adjusting the recipe once a day, to autonomously adjusting for every run. The whole idea behind these adjustments is for variability reduction and drift management, and customers weigh the targeted results versus the perceived risk of taking a novel approach.

SE: Does this involve building confidence in AI-based approaches?

Herlocker: Absolutely, and our systems have a large number of fail-safes, and some limits are hard-coded. We have people with PhDs in chemical engineering and material science who have operated these tools for years. These experts understand the physics of what is happening in these tools, and they have the practical experience to know what level of change can be expected or not.

SE: How much of your modeling is physics-based?

Herlocker: In the beginning, all of our modeling was physics-based, because we were working with equipment makers on their next-generation tools. But now we are also bringing our technology to device makers, where we can also deliver a lot of value by squeezing the most juice out of a data-driven approach. The main challenge with physics models is they are usually IP-protected. When we work with equipment makers, they typically pay us to build those physics-based models so they cannot be shared with other customers.

SE: So are your target customers the toolmakers or the fabs?

Herlocker: They are both our target customers. Most of our sales and marketing efforts are focused on device makers with legacy fabs. In most cases, the fab manager has us engage with their team members to do an assessment. Frequently, that team includes a cross section of automation, process, and equipment teams. The automation team is most interested in reducing the time to detect some sort of deviation that is going to cause yield loss, scrap, or tool downtime. The process and equipment engineers are interested in reducing variability or controlling drift, which also increases chamber life.

For example, let’s consider a PECVD tool. As I mentioned, every time you run the process, byproducts such as polymer materials build up on the chamber walls. You want a thickness of x in your deposition, but you are getting a slightly different wafer thickness uniformity due to drift of that chamber because of plasma confinement changes. Eventually, you must shut down the tool, wet clean the chamber, replace the preventive maintenance kit parts, and send them through the cleaning loop (i.e., to the cleaning vendor shop). Then you need to season the chamber and bring it back online. By controlling the process better, the PECVD team does not have to vent the chamber as often to clean parts. Just a 5% increase in chamber life can be quite significant from a maintenance cost reduction perspective (e.g., parts spend, refurb spend, cleaning spend, etc.). Reducing variability has a similarly large impact, particularly if it is a bottleneck tool, because then that reduction directly contributes to higher or more stable yields via more ‘sweet spot’ processing time, and sometimes better wafer throughput due to the longer chamber lifetime. The ROI story is more nuanced on non-bottleneck tools because they don’t modulate fab revenue, but the ROI there is still there. It is just more about chamber life stability.

SE: Where does this go next?

Herlocker: We also are working with OEMs on next-generation toolsets. Using AI/ML as the core of process control enables equipment makers to control processes that are impossible to implement with existing control strategies and software. For example, imagine on each process step there are a million different parameters that you can control. Further imagine that changing any one parameter has a global effect on all the other parameters, and only by co-varying all the million parameters in just the right way will you get the ideal outcome. And to further complicate things, toss in run-to-run variance, so that the right solution continues to change over time. And then there is the need to do this more than 200 times per hour to support high-volume manufacturing. AI/ML enables this kind of process control, which in turn will enable a step function increase in the ability to produce more complex devices more reliably.

SE: What additional changes do you see from AI-based algorithms?

Herlocker: Machine learning will dramatically improve the agility and productivity of the facility broadly. For example, process engineers will spend less time chasing issues and have more time to implement continuous improvement. Maintenance engineers will have time to do more preventive maintenance. Agility and resiliency — the ability to rapidly adjust to or maintain operations, despite disturbances in the factory or market — will increase. If you look at ML combined with upcoming generative AI capabilities, within a year or two we are going to have agents that effectively will understand many aspects of how equipment or a process works. These agents will make good engineers great, and enable better capture, aggregation, and transfer of manufacturing knowledge. In fact, we have some early examples of this running in our labs. These ML agents capture and ingest knowledge very quickly. So when it comes to implementing the vision of smart factories, machine learning automation will have a massive impact on manufacturing in the future.

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