Project Description

In the era of hybrid cloud, AI, and the Internet of Things, the demand for faster and more energy efficient microprocessors continues to rise. In order to address this growing demand, the newest IBM innovation projects to host 50 billion transistors on a chip the size of a fingernail, utilizing the latest 2 nanometer (nm) nanosheet node technology. However, self-heating poses one of the biggest challenges to realize these chip architectures, and leads to performance degradation. Scaling the nodes down from 14 nm to 7 nm results in a 20%-50% increase in heat confinement, and uncontrollable thermal properties at the nanometer length scale. This project will discover strategies to tune nanoscale thermal properties of new nanostructured FinFETs (field effect transistors) to desired specifications. The passive thermal control of FinFETs will help realize faster, more reliable, and more energy efficient chips, that can help slash the carbon footprint of data centers, which account for 1% of global energy use. Faster chips will also improve object detection rate and reaction time in autonomous vehicles like self-driving cars.

Special Requirement

Student must have experience with MATLAB/Python and/or other programming languages. A strong mathematics background and some basic physics/chemistry knowledge are also desired. The student should have maintained at least a 3.5 GPA. Some fundamental knowledge of thermal properties will be helpful but not required.

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