Error estimation improves multiscale models

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Semiconductor simulation is the test case

Oden is testing the adaptive modeling code by modeling a semiconductor production method developed by Texas engineering professor C. Grant Willson.  The process pushes a quartz template into material on a silicon chip.  Ultraviolet light shines through the template, converting the material to a polymer and making it denser in places.  The imprint creates the chip’s electronic circuitry.

Oden’s model simulates the polymerization and densification process at the molecular level.  “We’re looking at how you simulate events that take place on a macroscale – the scale of manufacturing the device – that result from activities that occur at the molecular scale – nanoscale and smaller,” he adds.  The simulation could help optimize the manufacturing process.

Semiconductors are only the first of what could be a long line of applications for the adaptive modeling code, Oden says.  “We can look at many, many different chemical constituencies and add different features to the model.  This is just the building block for more sophisticated models,” he adds.

The code runs on computer clusters of about 50 processors, Oden says, but has been projected to run well on systems of up to 100 processors.  The plan is to try it on Lonestar, the University of Texas supercomputer with a top speed of 55 trillion calculations per second.

“We’ll take it to the limit within a year,” Oden adds.

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