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Carl V. Thompson

Researcher at Massachusetts Institute of Technology

Publications -  422
Citations -  22680

Carl V. Thompson is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Thin film & Grain growth. The author has an hindex of 77, co-authored 416 publications receiving 21156 citations. Previous affiliations of Carl V. Thompson include Max Planck Society & Harvard University.

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Effects of microstructure on the formation, shape, and motion of voids during electromigration in passivated copper interconnects

TL;DR: In this paper, postmortem electron backscattered diffraction (EBSD) analysis was performed after in situ testing, and a correlation of EBSD data with the in situ observations reveals that locations at which voids form, their shape evolution, and their motion all strongly depend on the locations of grain boundaries and the crystallographic orientations of neighboring grains.
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Experimental characterization and modeling of the mechanical properties of Cu–Cu thermocompression bonds for three-dimensional integrated circuits

TL;DR: In this article, an analytical model is proposed which relates the bonding temperature, pressure and duration with the integrity of metal-metal thermocompression bonds, taking into account the pressure-dependent time evolution of the thermoco-compression bond formation.
Proceedings ArticleDOI

Modeling and experimental characterization of electromigration in interconnect trees

TL;DR: In this article, the authors extended the understanding of "immortality" demonstrated and analyzed for straight stud-to-stud lines, to trees of arbitrary complexity, and developed simulation tools that allow modeling of stress evolution and failure in arbitrarily complex trees.
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Porosimetry and packing morphology of vertically aligned carbon nanotube arrays via impedance spectroscopy.

TL;DR: The results suggest that waviness of CNTs leads to variations in the inter-CNT spacing, which can be significant in sparse carpets, and this methodology can be used to predict the performance of many nanostructured devices, including supercapacitors, batteries, solar cells, and semiconductor electronics.