Semiconductor fabs are leveraging AI/ML models and raw equipment data to speed up yield learning, but the volume of data they are collecting — in some cases, hundreds of petabytes a year — is forcing some tough decisions about optimization versus context.

The amount of data they are wrestling with has exploded due to increased sampling from inspection and metrology tools, which is used to detect yield issues. This includes data that is streaming from equipment sensors, which is a relatively new contributor to the data volume. AI/ML inference models use this sensor data for in-situ analysis, which enables more responsive advanced process control (APC) and quicker identification of changes that could adversely impact downstream yield.

“Over the last five years, the equipment data segment has grown exponentially,” said Ashish Gupta, senior data engineering manager for automation at Intel Foundry. “This explosion is primarily the result of fabs engaging in shift-left efforts into in-situ, real-time process control.”

Others agree that the increased use of equipment data has become a source of exponential data growth.

“Equipment data has expanded along three dimensions,” said Jae Yong Park, vice president of enterprise software business at Onto Innovation. “Suppliers have added hundreds of parameters over the past few years, sampling rates have increased from about once per second to every tens of milliseconds, and engineers are increasingly working with raw trace data instead of summary data. Together, these changes have dramatically increased the volume of streaming data.”

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