Big data applications drive growing needs of big memory. These applications will require lower latency to access your data, as well as cheaper and massive memories to store your data. It has presented a new challenge for the semiconductor industry. This course discusses how various modern memory device technologies work. The topics include discussions of various state-ofthe-art volatile and nonvolatile memory device technologies and their limitations. To go beyond these limitations, the course explores emerging memory device technologies, including those that could be adopted by industry in the next decades in computers and mobile devices due to potential performance, density, power and cost advantages.
In addition, deep learning and neuromorphic computing algorithms do not run efficiently in state-of-the-art computer hardwares. Device technologies in future neuromorphic processors that mimic how human brain works, such as memristors, will be discussed. The realization of memristive functionalities is closely related to the emerging memory device technologies.
Prerequisite: The students are expected to have already completed an introductory level device course at the undergraduate level, such as EEE3396c here at UF or any equivalent course at other institutes.
Goals:
(1) Explore state-of-the-art memory technologies
(2) Introduce emerging memory technologies for future big data applications
(3) Understand mechanisms and limitations of each memory device technology
(4) Introduce memristive devices for neuromorphic computing
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