Enablers And Barriers For Connecting Diverse Data

Integrating diverse data is possible in some cases, but it’s still not easy.

More data is being collected at every step of the manufacturing process, raising the possibility of combining data in new ways to solve engineering problems. But this is far from simple, and combining results is not always possible.

The semiconductor industry’s thirst for data has created oceans of it from the manufacturing process. In addition, semiconductor designs large and small now have on-die circuitry, which provides additional electrical test information flowing into these data oceans. Engineering teams need to manage this deluge of data and they need to facilitate consuming the data by diverse engineering teams.

More data can be connected together than in the past, but not all of it and not 100% of the time. Even where it is possible, it often requires an up-front, concerted engineering effort. For any one product, connecting all data sources into one system or model is not necessarily practical or essential for everyday engineering.

The complexity of CMOS technologies has increased both the amount and the types of data collected during the manufacturing process. For the most part, engineers have used this data in a siloed fashion, particularly during wafer fabrication, the assembly process, and the associated test processes. To face the challenges of advanced CMOS process nodes (22nm and below), engineers increasingly have turned to merging different types of data to meet yield and quality goals. Examples include:

“The desire to combine many multi-faceted data sources has been prevalent for a long time. There is a direct correlation between having many different data sources available to analyze and achieving higher quality of results, since there is more data you can correlate together to help isolate issues,” said Guy Cortez, product marketing manager in digital design group at Synopsys. “However, what has held customers back is their inability to collect all of the various data — and if they had the data, understand how to parse, align, normalize, merge, and stack the data in a reasonable amount of time.”

On-die monitors, which are becoming more common in safety- and mission-critical applications, where devices are expected to perform consistently for longer lifetimes, have only added to the amount of data being generated.

“As we implement silicon lifecycle solutions, the issue of data management is very much a live one,” said Aileen Ryan, senior director of portfolio strategy, Tessent silicon lifecycle solutions at Siemens EDA. “There are now lots of data sources. The manufacturing process (including manufacturing test) is one. But functional, structural, and parametric monitors embedded in the chip can also gather information during the initial bring-up and debug phase, and right through the chip’s life in the field.”

This trend of combining diverse data sources also can be observed in mature technologies, which commonly have lower ASPs. In these technologies, combining diverse data sources enables engineers to respond more rapidly to yield excursions and to squeeze an additional 2% to 3% yield at final test. The same parallel can be observed in both advanced and mature packaging technologies.

“Advanced manufacturing facilities have been combining data for the purposes of operational improvement for more than 25 years. This is not a new activity for successful manufacturing facilities. In more recent years this activity has gone mainstream due to changes in the performance and advancements in architectural capabilities for large scale databases,” said Mike McIntyre, director of software product management at Onto Innovation. “As these databases can now exceed 100 or 200 terabytes, they are still effective at retrieving the requested data in a timely manner.