Python speeds up GaN models

-January 14, 2013

Anyone following developments in high-power RF knows that GaN is hot (pun intended). According to Yole, there is a great deal of R&D activity in this market, and, earlier this year the company projected the power GaN market will grow to nearly $10 million in 2012 and $500 million in 2016 (up from less than $2.5million in 2010). When new semiconductor processes are introduced, they are usually slowly integrated into device modeling and characterization tools. However, whenever a process takes off, the capabilities of the device characterization and modeling tools must keep pace, or even anticipate the needs of the new market.

For example, Agilent Technologies recently announced a new version of IC-CAP (integrated circuit characterization and analysis platform) for high-frequency device characterization and modeling, offering parameter extraction, data analysis, instrument control and interface responsiveness. It also now includes Angelov-GaN modeling and Python scripting. This announcement actually includes two noteworthy topics: GaN and Python.

Angelov-GaN is an industry-standard compact device model for GaN semiconductor devices. Since GaN devices typically operate at high power, it is important to be able to model thermal issues and their impacts on device characteristics. Designers working with GaN quickly realized that GaAs models were not good enough. Fortunately, Prof. I. Angelov at Chalmers University of Technology developed his Angelov-GaN model as an alternative.

In its IC-CAP 2013.01, Agilent has fully embraced the Angelov model with the W8533 Angelov-GaN extraction package. An interface lets users execute a step-by-step extraction flow to obtain model parameters. A turnkey flow aims to provide a quick-start modeling of GaN devices. Roberto Tinti, device modeling product manager with Agilent EEsof EDA explained that the company developed the extraction package in conjunction with some Japanese and US GaN partners, but he was unable to reveal company names.

The Agilent product uses Python scripting. So, why Python? The Python program  may soon lay claim to the most used interpreter language. (One that does not require users to compile and generate object code, like C or C++ does; instead, they are compiled at run time, like The MathWorks MATLAB.) Tinti says “interpreter languages … are the best tools to create custom applications within a platform.” Tinti thinks an advantage of Python is the support of a large, open-source community. “Some of our major customers have started to use Python for their projects, and we decided to started to support it natively in our platform in addition to our own native language, called PEL.” Python is 100X faster than Agilent’s Programming Extraction Language, the language that IC-CAP users have been using to customize their applications. EMPro, another product from Agilent EEsof EDA, also uses Python.

This new IC-CAP release also includes support of Smartspice simulations, as well as support for gain compression and two-tone intermodulation distortion measurements with Agilent’s PNA-X network analyzer. If you are interested, the software is available for download.

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