Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing

Abstract

Abstract With the advent of big data era, applications are more data-centric, and energy efficiency issues caused by frequent data interactions due to the physical separation of memory and computing will become more severe. Emerging technologies have been proposed to perform analog computing with memory to address the dilemma. Ferroelectric memory has become a promising technology due to field-driven fast switching and non-destructive readout, but endurance and miniaturization are limited. Here, we demonstrate the α-In2Se3 ferroelectric semiconductor channel device that integrates non-volatile memory (NVM) and neural computation functions. Remarkable performance includes NVM ultra-fast write speed of 40ns, improved endurance through internal electric field, flexible adjustment of neural plasticity, energy consumption as low as 40fJ per event, and thermally modulated 94.74% high-precision iris recognition classification simulation. This prototypical demonstration laid the foundation for an integrated memory computing system with high density and energy efficiency.

Abstract of "Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing" by Shuiyuan Wang, Lan Liu, Lurong Gan et al., https://doi.org/10.1038/s41467-020-20257-2, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.