Bidirectional Electric-Induced Conductance Based on GeTe/Sb<sub>2</sub>Te<sub>3</sub> Interfacial Phase Change Memory for Neuro-Inspired Computing
Corresponding to the principles of biological synapses, an essential prerequisite for hardware neural networks using electronics devices is the continuous regulation of conductance. We implemented artificial synaptic characteristics in a (GeTe/Sb<sub>2</sub>Te<sub>3</sub>)<...
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| Autores principales: | , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/15a44b6792884569943c766c7c820fa2 |
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| Sumario: | Corresponding to the principles of biological synapses, an essential prerequisite for hardware neural networks using electronics devices is the continuous regulation of conductance. We implemented artificial synaptic characteristics in a (GeTe/Sb<sub>2</sub>Te<sub>3</sub>)<sub>16</sub> iPCM with a superlattice structure under optimized identical pulse trains. By atomically controlling the Ge switch in the phase transition that appears in the GeTe/Sb<sub>2</sub>Te<sub>3</sub> superlattice structure, multiple conductance states were implemented by applying the appropriate electrical pulses. Furthermore, we found that the bidirectional switching behavior of a (GeTe/Sb<sub>2</sub>Te<sub>3</sub>)<sub>16</sub> iPCM can achieve a desired resistance level by using the pulse width. Therefore, we fabricated a Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> PCM and designed a pulse scheme, which was based on the phase transition mechanism, to compare to the (GeTe/Sb<sub>2</sub>Te<sub>3</sub>)<sub>16</sub> iPCM. We also designed an identical pulse scheme that implements both linear and symmetrical LTP and LTD, based on the iPCM mechanism. As a result, the (GeTe/Sb<sub>2</sub>Te<sub>3</sub>)<sub>16</sub> iPCM showed relatively excellent synaptic characteristics by implementing a gradual conductance modulation, a nonlinearity value of 0.32, and 40 LTP/LTD conductance states by using identical pulse trains. Our results demonstrate the general applicability of the artificial synaptic device for potential use in neuro-inspired computing and next-generation, non-volatile memory. |
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