Exploiting Ferroelectric FETs: From Logic-in-Memory to Neural Networks and Beyond

The inevitable slowdown of the CMOS scaling trend has fueled an explosion of research endeavors in finding a CMOS replacement. However, recent studies suggest that many of the emerging devices being investigated, if used as simple drop-in replacement for MOSFETs, may only achieve speedups that mirror historical trends in the best case. The consensus from the community is that cross-layer efforts are essential in combating the CMOS scaling challenge with emerging devices. This talk presents such an effort centered around a particular emerging device, ferroelectric FETs (FeFETs).

An FeFET is made by integrating a ferroelectric material layer in the gate stack of a MOSFET. It is a non- volatile device that can behave as both a transistor and a storage element. This unique property of FeFETs enables area efficient and low-power fine-grained logic-in-memory, which are desirable for many data analytic and machine learning applications. This presentation will elaborate novel circuits based on FeFETs to accomplish basic logic-in-memory operations, ternary content addressable memory (TCAM) as well as FeFET based crossbars for binary Convolutional Neural Networks. Comparisons of these FeFET based circuits with other alternative technologies will be discussed.