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Associative Learning Emulation in HZO-Based Ferroelectric Memristor Devices.

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Title: Associative Learning Emulation in HZO-Based Ferroelectric Memristor Devices.
Authors: Seo, Euncho1 (AUTHOR), Rasheed, Maria2 (AUTHOR), Kim, Sungjun1 (AUTHOR) sungjun@dongguk.edu
Source: Materials (1996-1944). Jul2025, Vol. 18 Issue 14, p3210. 10p.
Subjects: Associative learning, Short-term memory, Artificial intelligence, Memristors, Hafnium oxide, Artificial synapses, Artificial neural networks, Neuroplasticity
Abstract: Neuromorphic computing inspired by biological synapses requires memory devices capable of mimicking short-term memory (STM) and associative learning. In this study, we investigate a 15 nm-thick Hafnium zirconium oxide (HZO)-based ferroelectric memristor device, which exhibits robust STM characteristics and successfully replicates Pavlov's dog experiment. The optimized 15 nm HZO layer demonstrates enhanced ferroelectric properties, including a stable orthorhombic phase and a reliable short-term synaptic response. Furthermore, through a series of conditional learning experiments, the device effectively reproduces associative learning by forming and extinguishing conditioned responses, closely resembling biological neural plasticity. The number of training repetitions significantly affects the retention of learned responses, indicating a transition from STM-like behavior to longer-lasting memory effects. These findings highlight the potential of the optimized ferroelectric device in neuromorphic applications, particularly for implementing real-time learning and memory in artificial intelligence systems. [ABSTRACT FROM AUTHOR]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Associative Learning Emulation in HZO-Based Ferroelectric Memristor Devices.
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Jul2025, Vol. 18 Issue 14, p3210. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Associative+learning%22">Associative learning</searchLink><br /><searchLink fieldCode="DE" term="%22Short-term+memory%22">Short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Memristors%22">Memristors</searchLink><br /><searchLink fieldCode="DE" term="%22Hafnium+oxide%22">Hafnium oxide</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+synapses%22">Artificial synapses</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Neuroplasticity%22">Neuroplasticity</searchLink>
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  Label: Abstract
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  Data: Neuromorphic computing inspired by biological synapses requires memory devices capable of mimicking short-term memory (STM) and associative learning. In this study, we investigate a 15 nm-thick Hafnium zirconium oxide (HZO)-based ferroelectric memristor device, which exhibits robust STM characteristics and successfully replicates Pavlov's dog experiment. The optimized 15 nm HZO layer demonstrates enhanced ferroelectric properties, including a stable orthorhombic phase and a reliable short-term synaptic response. Furthermore, through a series of conditional learning experiments, the device effectively reproduces associative learning by forming and extinguishing conditioned responses, closely resembling biological neural plasticity. The number of training repetitions significantly affects the retention of learned responses, indicating a transition from STM-like behavior to longer-lasting memory effects. These findings highlight the potential of the optimized ferroelectric device in neuromorphic applications, particularly for implementing real-time learning and memory in artificial intelligence systems. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/ma18143210
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 3210
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      – SubjectFull: Associative learning
        Type: general
      – SubjectFull: Short-term memory
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Memristors
        Type: general
      – SubjectFull: Hafnium oxide
        Type: general
      – SubjectFull: Artificial synapses
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Neuroplasticity
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      – TitleFull: Associative Learning Emulation in HZO-Based Ferroelectric Memristor Devices.
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            NameFull: Seo, Euncho
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            NameFull: Rasheed, Maria
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            NameFull: Kim, Sungjun
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            – D: 15
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 18
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            – TitleFull: Materials (1996-1944)
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