A Reconfigurable Ferroelectric Transistor as An Ultra-Scaled Cell for Low-Power In-Memory Data Processing
(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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- author
- Zhu, Zhongyunshen LU ; Persson, Anton E.O. LU and Wernersson, Lars Erik LU
- organization
- publishing date
- 2024-08-05
- 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}}, }