Fraunhofer Institute for Integrated Circuits (Fraunhofer IIS)

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Erlangen, Germany

Our application-oriented research institute is a global leader in microelectronic and information technology system solutions and services. We are currently the largest institute within the Fraunhofer-Gesellschaft.

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Enhancing Datasets For Artificial Intelligence Through Model-Based Methods

Date:

Authors: Dirk Mayer, Ulf Wetzker

Topics: artificial intelligence

Organizations: Fraunhofer IIS

In industrial processes, data from time series play a particularly important role (e.g., sensor data, process parameters, log files, communication protocols). They are available in very different temporal resolutions – a temperature sensor might deliver values every minute, while for a spectral analysis of wireless network requires over 100 million samples per second.

The objective is to reflect all relevant states of the processes and uncertainties due to stochastic effects within the augmented time series. To add additional values to measured time series of an industrial process, insights into the process are beneficial. Such representation of the physical background can be called a model.

Read more at Semi Engineering

AI In Inspection, Metrology, And Test

Date:

Authors: Susan Rambo, Ed Sperling

Topics: AI, machine learning, quality assurance, metrology, nondestructive test

Vertical: Semiconductor

Organizations: CyberOptics, Lam Research, Hitachi, FormFactor, NuFlare, Advantest, PDF Solutions, eBeam Initiative, KLA, proteanTecs, Fraunhofer IIS

“The human eye can see things that no amount of machine learning can,” said Subodh Kulkarni, CEO of CyberOptics. “That’s where some of the sophistication is starting to happen now. Our current systems use a primitive kind of AI technology. Once you look at the image, you can see a problem. And our AI machine doesn’t see that. But then you go to the deep learning kind of algorithms, where you have very serious Ph.D.-level people programming one algorithm for a week, and they can detect all those things. But it takes them a week to program those things, which today is not practical.”

That’s beginning to change. “We’re seeing faster deep-learning algorithms that can be more easily programmed,” Kulkarni said. “But the defects also are getting harder to catch by a machine, so there is still a gap. The biggest bang for the buck is not going to come from improving cameras or projectors or any of the equipment that we use to generate optical images. It’s going to be interpreting optical images.”

Read more at Semiconductor Engineering