Associative Learning Emulation in HZO-Based Ferroelectric Memristor Devices.
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| Title: | Associative Learning Emulation in HZO-Based Ferroelectric Memristor Devices. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 19961944 |
| DOI: | 10.3390/ma18143210 |
