BEOL Compatible Superlattice FerroFET-based High Precision Analog Weight Cell with Superior Linearity and Symmetry

Khandker Akif Aabrar*, Jorge Gomez, Sharadindu Gopal Kirtania, Matthew San Jose, Yandong Luo, Priyankka G. Ravikumar, Prasanna Venkatesan Ravindran, Huacheng Ye, Sanjukta Banerjee, Sourav Dutta, Asif Islam Khan, Shimeng Yu, Suman Datta

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

24 Citas (Scopus)

Resumen

Off-chip DRAM memory accesses limit the energy efficiency and training time of state-of-The-Art deep neural networks (DNN). Compute-in-memory (CIM) accelerators leveraging pseudo-crossbar arrays and on-chip weight storage have emerged as alternatives to GPUs for fast and efficient training. However, this comes at the cost of reduced training accuracy due to weight cell non-idealities such as: low bit precision, nonlinearity, asymmetry, low Gmax/Gmin ratio, and slow programming speed. Here, we engineer the ferroelectric domain structure in a carefully designed superlattice (SL) ferroelectric(FE)/dielectric(DE) stack, to experimentally demonstrate high precision FEFET analog weight cells with excellent linearity and symmetry during potentiation and depression. We demonstrate switching speed as low as 100 ns in the SL-based ferroelectric capacitor (FECAP), with no degradation in either retention or endurance. We integrate the SL FE/DE/FE with a back-end-of-line (BEOL) compatible Indium Tungsten Oxide transistors, to demonstrate 128 stable conductance states with improved linearity and symmetry. System-level analysis of SL-FEFET based CIM accelerators show an excellent 94.1% online learning accuracy without degrading any other performance parameter, with potential for monolithic 3D integration.

Idioma originalInglés
Título de la publicación alojada2021 IEEE International Electron Devices Meeting, IEDM 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas19.6.1-19.6.4
ISBN (versión digital)9781665425728
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento2021 IEEE International Electron Devices Meeting, IEDM 2021 - San Francisco, Estados Unidos
Duración: 11 dic. 202116 dic. 2021

Serie de la publicación

NombreTechnical Digest - International Electron Devices Meeting, IEDM
Volumen2021-December
ISSN (versión impresa)0163-1918

Conferencia

Conferencia2021 IEEE International Electron Devices Meeting, IEDM 2021
País/TerritorioEstados Unidos
CiudadSan Francisco
Período11/12/2116/12/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

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