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A Reconfigurable Ferroelectric Transistor as An Ultra-Scaled Cell for Low-Power In-Memory Data Processing

Zhu, Zhongyunshen LU orcid ; Persson, Anton E.O. LU orcid and Wernersson, Lars Erik LU (2024) In Advanced Electronic Materials
Abstract

Compact in-memory computing architectures are desirable to embed artificial intelligence (AI) in resource-restricted edge devices. However, current technologies face limitations in both the area and energy efficiency. Here, a reconfigurable ferroelectric tunnel field-effect transistor (ferro-TFET) is presented that can be used as an ultra-scaled cell for low-power in-memory data processing. A gate-all-around ferroelectric film is integrated on a vertical nanowire TFET with a gate/source overlapped channel, enabling non-volatilely reconfigurable anti-ambipolarity by programming the ferroelectric polarization state. By considering the stored polarization state and reading voltage as inputs, an XNOR operation is achieved in a single-gate... (More)

Compact in-memory computing architectures are desirable to embed artificial intelligence (AI) in resource-restricted edge devices. However, current technologies face limitations in both the area and energy efficiency. Here, a reconfigurable ferroelectric tunnel field-effect transistor (ferro-TFET) is presented that can be used as an ultra-scaled cell for low-power in-memory data processing. A gate-all-around ferroelectric film is integrated on a vertical nanowire TFET with a gate/source overlapped channel, enabling non-volatilely reconfigurable anti-ambipolarity by programming the ferroelectric polarization state. By considering the stored polarization state and reading voltage as inputs, an XNOR operation is achieved in a single-gate ferro-TFET. It is shown that the ferro-TFETs can be implemented in a crossbar array for convolutional frequency filtering whose performance can be evaluated by an impulse-response method considering the effect of device-to-device variation based on statistics. Benefiting from the miniaturized footprint, non-volatility, and low-power operation, ferro-TFETs show promises as a one-transistor in-memory computing cell for area- and energy-efficient edge AI applications.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
ferroelectric, in-memory data processing, reconfigurable, ultra-scaled
in
Advanced Electronic Materials
publisher
Wiley-Blackwell
external identifiers
  • scopus:85200234102
ISSN
2199-160X
DOI
10.1002/aelm.202400335
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 The Author(s). Advanced Electronic Materials published by Wiley-VCH GmbH.
id
5e8c4599-f88f-4b45-b314-ce500fd046e5
date added to LUP
2024-08-19 20:26:15
date last changed
2024-08-21 08:27:30
@article{5e8c4599-f88f-4b45-b314-ce500fd046e5,
  abstract     = {{<p>Compact in-memory computing architectures are desirable to embed artificial intelligence (AI) in resource-restricted edge devices. However, current technologies face limitations in both the area and energy efficiency. Here, a reconfigurable ferroelectric tunnel field-effect transistor (ferro-TFET) is presented that can be used as an ultra-scaled cell for low-power in-memory data processing. A gate-all-around ferroelectric film is integrated on a vertical nanowire TFET with a gate/source overlapped channel, enabling non-volatilely reconfigurable anti-ambipolarity by programming the ferroelectric polarization state. By considering the stored polarization state and reading voltage as inputs, an XNOR operation is achieved in a single-gate ferro-TFET. It is shown that the ferro-TFETs can be implemented in a crossbar array for convolutional frequency filtering whose performance can be evaluated by an impulse-response method considering the effect of device-to-device variation based on statistics. Benefiting from the miniaturized footprint, non-volatility, and low-power operation, ferro-TFETs show promises as a one-transistor in-memory computing cell for area- and energy-efficient edge AI applications.</p>}},
  author       = {{Zhu, Zhongyunshen and Persson, Anton E.O. and Wernersson, Lars Erik}},
  issn         = {{2199-160X}},
  keywords     = {{ferroelectric; in-memory data processing; reconfigurable; ultra-scaled}},
  language     = {{eng}},
  month        = {{08}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{Advanced Electronic Materials}},
  title        = {{A Reconfigurable Ferroelectric Transistor as An Ultra-Scaled Cell for Low-Power In-Memory Data Processing}},
  url          = {{http://dx.doi.org/10.1002/aelm.202400335}},
  doi          = {{10.1002/aelm.202400335}},
  year         = {{2024}},
}