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Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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NGExtract 2: MOSFET Parameter Extraction Tool

Description
NGExtract 2 is a complete transistor (MOSFET) parameter extraction solution based upon the original computer program NGExtract by Rahul Shringarpure written in February 2007. NGExtract 2 is written in Java and based around the circuit simulator NGSpice. The goal of the program is to be used to produce…

NGExtract 2 is a complete transistor (MOSFET) parameter extraction solution based upon the original computer program NGExtract by Rahul Shringarpure written in February 2007. NGExtract 2 is written in Java and based around the circuit simulator NGSpice. The goal of the program is to be used to produce accurate transistor models based around real-world transistor data. The program contains numerous improvements to the original program:
• Completely rewritten with performance and usability in mind
• Cross-Platform vs. Linux Only
• Simple installation procedure vs. compilation and manual library configuration
• Self-contained, single file runtime
• Particle Swarm Optimization routine
NGExtract 2 works by plotting the Ids vs. Vds and Ids vs. Vgs curves of a simulation model and the measured, real-world data. The user can adjust model parameters and re-simulate to attempt to match the curves. The included Particle Swarm Optimization routine attempts to automate this process by iteratively attempting to improve a solution by measuring its sum-squared error against the real-world data that the user has provided.
ContributorsVetrano, Michael Thomas (Author) / Allee, David (Thesis director) / Gorur, Ravi (Committee member) / Bakkaloglu, Bertan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05