Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applications
Abstract Memristors, owing to their uncomplicated structure and resemblance to biological synapses, are predicted to see increased usage in the domain of artificial intelligence. Additionally, to augment the capacity for multilayer data storage in high-density memory applications, meticulous regulat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-07-01
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| Series: | Nano Convergence |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40580-023-00380-8 |
| _version_ | 1827899686637273088 |
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| author | Muhammad Ismail Maria Rasheed Chandreswar Mahata Myounggon Kang Sungjun Kim |
| author_facet | Muhammad Ismail Maria Rasheed Chandreswar Mahata Myounggon Kang Sungjun Kim |
| author_sort | Muhammad Ismail |
| collection | DOAJ |
| description | Abstract Memristors, owing to their uncomplicated structure and resemblance to biological synapses, are predicted to see increased usage in the domain of artificial intelligence. Additionally, to augment the capacity for multilayer data storage in high-density memory applications, meticulous regulation of quantized conduction with an extremely low transition energy is required. In this work, an a-HfSiOx-based memristor was grown through atomic layer deposition (ALD) and investigated for its electrical and biological properties for use in multilevel switching memory and neuromorphic computing systems. The crystal structure and chemical distribution of the HfSiOx/TaN layers were analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The Pt/a-HfSiOx/TaN memristor was confirmed by transmission electron microscopy (TEM) and showed analog bipolar switching behavior with high endurance stability (1000 cycles), long data retention performance (104 s), and uniform voltage distribution. Its multilevel capability was demonstrated by restricting current compliance (CC) and stopping the reset voltage. The memristor exhibited synaptic properties, such as short-term plasticity, excitatory postsynaptic current (EPSC), spiking-rate-dependent plasticity (SRDP), post-tetanic potentiation (PTP), and paired-pulse facilitation (PPF). Furthermore, it demonstrated 94.6% pattern accuracy in neural network simulations. Thus, a-HfSiOx-based memristors have great potential for use in multilevel memory and neuromorphic computing systems. Graphical Abstract |
| first_indexed | 2024-03-12T23:21:17Z |
| format | Article |
| id | doaj.art-5c1a18c6a0ef48578ffcd1ae1ff54432 |
| institution | Directory Open Access Journal |
| issn | 2196-5404 |
| language | English |
| last_indexed | 2024-03-12T23:21:17Z |
| publishDate | 2023-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Nano Convergence |
| spelling | doaj.art-5c1a18c6a0ef48578ffcd1ae1ff544322023-07-16T11:24:38ZengSpringerOpenNano Convergence2196-54042023-07-0110111510.1186/s40580-023-00380-8Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applicationsMuhammad Ismail0Maria Rasheed1Chandreswar Mahata2Myounggon Kang3Sungjun Kim4Division of Electronics and Electrical Engineering, Dongguk UniversityDivision of Electronics and Electrical Engineering, Dongguk UniversityDivision of Electronics and Electrical Engineering, Dongguk UniversityDepartment of Electronics Engineering, Korea National University of TransportationDivision of Electronics and Electrical Engineering, Dongguk UniversityAbstract Memristors, owing to their uncomplicated structure and resemblance to biological synapses, are predicted to see increased usage in the domain of artificial intelligence. Additionally, to augment the capacity for multilayer data storage in high-density memory applications, meticulous regulation of quantized conduction with an extremely low transition energy is required. In this work, an a-HfSiOx-based memristor was grown through atomic layer deposition (ALD) and investigated for its electrical and biological properties for use in multilevel switching memory and neuromorphic computing systems. The crystal structure and chemical distribution of the HfSiOx/TaN layers were analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The Pt/a-HfSiOx/TaN memristor was confirmed by transmission electron microscopy (TEM) and showed analog bipolar switching behavior with high endurance stability (1000 cycles), long data retention performance (104 s), and uniform voltage distribution. Its multilevel capability was demonstrated by restricting current compliance (CC) and stopping the reset voltage. The memristor exhibited synaptic properties, such as short-term plasticity, excitatory postsynaptic current (EPSC), spiking-rate-dependent plasticity (SRDP), post-tetanic potentiation (PTP), and paired-pulse facilitation (PPF). Furthermore, it demonstrated 94.6% pattern accuracy in neural network simulations. Thus, a-HfSiOx-based memristors have great potential for use in multilevel memory and neuromorphic computing systems. Graphical Abstracthttps://doi.org/10.1186/s40580-023-00380-8a-HfSiOx filmAnalog tunable switchingExcitatory postsynaptic currentSpiking-rate-dependent plasticitySchottky emission |
| spellingShingle | Muhammad Ismail Maria Rasheed Chandreswar Mahata Myounggon Kang Sungjun Kim Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applications Nano Convergence a-HfSiOx film Analog tunable switching Excitatory postsynaptic current Spiking-rate-dependent plasticity Schottky emission |
| title | Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applications |
| title_full | Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applications |
| title_fullStr | Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applications |
| title_full_unstemmed | Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applications |
| title_short | Mimicking biological synapses with a-HfSiOx-based memristor: implications for artificial intelligence and memory applications |
| title_sort | mimicking biological synapses with a hfsiox based memristor implications for artificial intelligence and memory applications |
| topic | a-HfSiOx film Analog tunable switching Excitatory postsynaptic current Spiking-rate-dependent plasticity Schottky emission |
| url | https://doi.org/10.1186/s40580-023-00380-8 |
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