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Prospects of analog in-memory computing using ferroelectric tunnel junctions

Borg, Mattias LU orcid ; Papadopoulos, Christos ; Guerin, Alec ; Athle, Robin LU and Bastani, Saeed LU (2025) In Neuromorphic Computing and Engineering 5(2).
Abstract

Artificial intelligence (AI) is set to disrupt the way businesses and civil society operate, but the large energy usage for training and running AI compute models remains a pressing concern in terms of its sustainability. Analog in-memory computing (AIMC) hardware based on memristor devices is a promising route to significant energy-savings. Among the various memristor technologies, ferroelectric tunnel junctions (FTJs) hold considerable promise for large scale AIMC. Their performance prospects remain to be fully assessed; therefore, we here evaluate the viability of FTJ memristors as compute units for AIMC. Based on the behavior of experimental TiN/HfZrO4/W FTJs we define three operating modes which we apply to standard AI... (More)

Artificial intelligence (AI) is set to disrupt the way businesses and civil society operate, but the large energy usage for training and running AI compute models remains a pressing concern in terms of its sustainability. Analog in-memory computing (AIMC) hardware based on memristor devices is a promising route to significant energy-savings. Among the various memristor technologies, ferroelectric tunnel junctions (FTJs) hold considerable promise for large scale AIMC. Their performance prospects remain to be fully assessed; therefore, we here evaluate the viability of FTJ memristors as compute units for AIMC. Based on the behavior of experimental TiN/HfZrO4/W FTJs we define three operating modes which we apply to standard AI tests and two real-world use cases: Image segmentation (YOLOv8) and natural language processing (BERT). We find that the inherent mechanism behind the analog state in FTJs limits the usable dynamic range and thus enforces strict control of noise. The best overall performance is obtained by constricting the dynamic range further and using ultrashort programming pulses (0.5 ns); on BERT matching the performance of digital hardware. We also present a more accessible approach, combining three FTJs, two of which are operated as binary memories, to achieve similar performance while also relaxing the requirements on data converter precision and level of noise. All in all, we here reveal benefits and intrinsic challenges of FTJ-based AIMC systems, providing a blueprint for future experimental implementations.

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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
published
subject
keywords
analog in-memory computing (AIMC), BERT, ferroelectric tunnel junction (FTJ), hafnium zirconate (HZO), memristor, neuromorphic computing, Yolo
in
Neuromorphic Computing and Engineering
volume
5
issue
2
article number
024006
publisher
IOP Publishing
external identifiers
  • scopus:105005143211
ISSN
2634-4386
DOI
10.1088/2634-4386/add0d9
project
Robust neuromorphic computing using ferroelectric memristors
Ultra-fast thermal processing for next-generation ferroelectric hafnia
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 The Author(s). Published by IOP Publishing Ltd.
id
17fc1c09-b564-480d-9e1f-dbd55a93784a
date added to LUP
2025-06-09 11:30:00
date last changed
2025-06-10 12:19:29
@article{17fc1c09-b564-480d-9e1f-dbd55a93784a,
  abstract     = {{<p>Artificial intelligence (AI) is set to disrupt the way businesses and civil society operate, but the large energy usage for training and running AI compute models remains a pressing concern in terms of its sustainability. Analog in-memory computing (AIMC) hardware based on memristor devices is a promising route to significant energy-savings. Among the various memristor technologies, ferroelectric tunnel junctions (FTJs) hold considerable promise for large scale AIMC. Their performance prospects remain to be fully assessed; therefore, we here evaluate the viability of FTJ memristors as compute units for AIMC. Based on the behavior of experimental TiN/HfZrO<sub>4</sub>/W FTJs we define three operating modes which we apply to standard AI tests and two real-world use cases: Image segmentation (YOLOv8) and natural language processing (BERT). We find that the inherent mechanism behind the analog state in FTJs limits the usable dynamic range and thus enforces strict control of noise. The best overall performance is obtained by constricting the dynamic range further and using ultrashort programming pulses (0.5 ns); on BERT matching the performance of digital hardware. We also present a more accessible approach, combining three FTJs, two of which are operated as binary memories, to achieve similar performance while also relaxing the requirements on data converter precision and level of noise. All in all, we here reveal benefits and intrinsic challenges of FTJ-based AIMC systems, providing a blueprint for future experimental implementations.</p>}},
  author       = {{Borg, Mattias and Papadopoulos, Christos and Guerin, Alec and Athle, Robin and Bastani, Saeed}},
  issn         = {{2634-4386}},
  keywords     = {{analog in-memory computing (AIMC); BERT; ferroelectric tunnel junction (FTJ); hafnium zirconate (HZO); memristor; neuromorphic computing; Yolo}},
  language     = {{eng}},
  month        = {{06}},
  number       = {{2}},
  publisher    = {{IOP Publishing}},
  series       = {{Neuromorphic Computing and Engineering}},
  title        = {{Prospects of analog in-memory computing using ferroelectric tunnel junctions}},
  url          = {{http://dx.doi.org/10.1088/2634-4386/add0d9}},
  doi          = {{10.1088/2634-4386/add0d9}},
  volume       = {{5}},
  year         = {{2025}},
}