Litcius/Paper detail

HfO<sub>2</sub>-based FeFET and FTJ for Ferroelectric-Memory Centric 3D LSI towards Low-Power and High-Density Storage and AI Applications

Masumi Saitoh, Reika Ichihara, M. Yamaguchi, Kunifumi Suzuki, Keisuke Takano, Keisuke Akari, Kota Takahashi, Yuta Kamiya, Kazuhiro Matsuo, Yuuichi Kamimuta, Kiwamu Sakuma, Kensuke Ota, Shosuke Fujii

202032 citationsDOI

Abstract

We present the recent progress in HfO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -based ferroelectric FET (FeFET) and ferroelectric tunnel junction (FTJ) memory towards low-power and high-density storage and AI applications. A huge amount of interface trap charges coupled to spontaneous polarization significantly alters the operating model and improvement guideline of HfO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> FeFET irrespective of elements doped into HfO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . Performance and reliability of in-memory reinforcement learning (RL) with HfO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> FTJ array are enhanced by improving the characteristics of FTJ memory cells.

Topics & Concepts

FerroelectricityDopingMaterials scienceElectrical engineeringComputer scienceOptoelectronicsEngineeringDielectricFerroelectric and Negative Capacitance DevicesMXene and MAX Phase MaterialsAdvanced Memory and Neural Computing