Python speeds up GaN models

摘要

Anyone following developments in high-power RF knows that GaN is hot (pun intended). According to research firm Yole Developpement, there is a great deal of R&D activity in this market. In March 2012, the company projected the power GaN market would grow to nearly $10 million in 2012 and $500 million in 2016-up from less than $2.5 million in 2010. When new semiconductor processes are introduced, they are usually slowly integrated into device modeling and characterization tools. If a process takes off, however, the capabilities of the device modeling and characterization tools must keep pace, or even anticipate the needs of the new market. Agilent Technologies, for example, recently announced a new version of its Integrated Circuit Characterization and Analysis Program (IC-CAP) 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, Professor lltcho Angelov at Chalmers University of Technology (Gothenburg, Sweden) developed his Angelov-GaN model as an alternative.