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Applied Materials designs tools to leverage big data and produce superior chips

In semiconductor manufacturing, metrology – the science of measuring things – is an absolutely vital part of the manufacturing process. Much of this analysis is handled by CDSEM (Critical Dimension Scanning Electron Microscopy) equipment. As process nodes shrink and manufacturing difficulty increases, the amount of data being collected per wafer has increased.

Foundries now collect more data per wafer than ever before, and they need to be able to analyse that information quickly and compare it to other readouts from different pieces of equipment. Applied Materials has launched a new web backend it calls TechEdge Prizm that’s designed to offer foundries better data on their day-to-day production, and to do so in a far superior manner than what’s currently available.

Note that the following image is drawn from an IBM 2013 SPIE paper from a study by Eric Solecky et al: SPIE 8681, Metrology, Inspection, and Process Control for Microlithography XXVII, 86810D (April 10, 2013); doi:10.1117/12.2010007:

With the amount of data per fab skyrocketing from 50TB per fab per year at 45nm to 80TB at 28nm, and an estimated 141TB at 14nm, better tools are needed for visualising and examining system output closer to real-time. In the past, data was gathered by individual tools, locally stored, and painful to parse. There was no unified system for collecting information or comparing results between tools or across longer periods of time.

With Prizm, Applied Materials hopes to change that. Instead of trying to parse data sets on a tool-by-tool basis, Prizm can gather data from multiple tools and present it through a unified interface. Results are searchable and can be analysed much more quickly. Total time savings, again according to Applied Materials, are shown below (the green bar represents the analysis time with Prizm, the red bar is the current time):

Prizm allows engineers to see various metrics on individual sections of a wafer map rather than simply as a chart of total data. Prizm is capable of showing how specific metrics have changed over time, and it can compare specific metrics from one set of wafers against a later set. According to Applied Materials, Prizm can improve workflow efficiency by a factor of 10 in certain cases, and spare engineers hours of tedious work manually gathering data. The online backend also stores data far longer – typical tools preserve data sets for a month; Applied Materials is guaranteeing seven years of storage for particular tools.

We spoke to Applied Materials about Prizm, and the company offered us a remote demo of how the service works. In the screenshot above, the engineer is able to drill down to examine metrics at each specific point on the wafer.

Clicking on a section brings up an image of that area and gives more information on the selected metrics. The entire system is designed for flexibility – the engineers can examine and sort by tool type, process node, or a specific quality measure.

When Big Data matters

I’m sceptical of “big data” for the same reasons I’m sceptical of “cloud computing,” but the dramatic overuse and subsequent dilution of the latter phrase doesn’t mean there aren’t cases where cloud computing hasn’t offered something unique and different compared to the services we used to have.

In this case, the term “big data” term also seems to fit. Not only do these tools produce a staggering amount of information, the ability to sift and sort said research is essential to progress.

We’ve previously discussed the mind-boggling levels of accuracy the modern semiconductor industry requires as a matter of course, and the ability to measure those levels accurately is a necessity if products are to continue pushing below 20nm. Improving data collection and analysis doesn’t directly solve the problems facing the semiconductor industry, but it does ensure that the researchers working at companies like Intel, TSMC, and GlobalFoundries have access to the data they need to investigate defects more swiftly.