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Right now, increasing throughput is top of mind for semiconductor FABs, as they attempt to overcome the challenges of the global chip shortage.

Looking beyond this, there are significant opportunities for long term cost savings that can come from optimizing, simplifying or removing processing steps in semiconductor manufacturing.

<p-sub>To achieve these improvements, there’s a need for real-time, accurate and actionable data.<p-sub>

We call this Equipment and Process Co-Optimization, or EPCO for short. It's a combination of good engineering, and applying machine learning (ML) to the manufacturing process and equipment.  

For example, statistical process controls can look at the real effects of chamber to chamber, machine and run to run differences that you see, even on the exact same equipment with the same recipe. Process control has become a lot more complicated as critical dimensions have shrunk, along with the margin for error. This means that individual chamber management is becoming fundamental to ensuring high line yield, with tight statistical process control.  

<p-center>This is what EPCO is all about: ensuring the equipment, chambers and the process are optimized together, often using advanced machine learning techniques.<p-center>

Atonarp has spent a lot of time understanding the FAB and equipment manufacturers’ problems and challenges. The result of those efforts is Aston – our robust molecular sensor. 

Aston provides the accurate, actionable, real-time data that’s critical for effective EPCO. This data enables suitable ML models to be built and tested.

In fact, Aston was designed from the ground up to meet the needs of in-situ molecular analysis to enable EPCO.

Read more below in our white paper on EPCO for semiconductor manufacturing.