We perform a comparative study of HfO2 and Ta2O5 resistive switching memory (RRAM) devices for their possible application as electronic synapses. By means of electrical characterization and simulations, we link their electrical behavior (digital or analog switching) to the properties and evolution of the conductive filament (CF). More specifically, we identify that bias-polarity-dependent digital switching in HfO2 RRAM is primarily related to the creation and rupture of an oxide barrier. Conversely, the modulation of the CF size in Ta2O5 RRAM allows bias-polarity-independent analog switching with multiple states. Therefore, when the Ta2O5 RRAM is used to implement a synapse in multilayer perceptron neural networks operated by back-propagation algorithms, patterns in handwritten digits can be recognized with high accuracy.
Linking Conductive Filament Properties and Evolution to Synaptic Behavior of RRAM Devices for Neuromorphic Applications / Woo, Jiyong; Padovani, Andrea; Moon, Kibong; Kwak, Myounghun; Larcher, Luca; Hwang, Hyunsang. - In: IEEE ELECTRON DEVICE LETTERS. - ISSN 0741-3106. - 38:9(2017), pp. 1220-1223. [10.1109/LED.2017.2731859]
Linking Conductive Filament Properties and Evolution to Synaptic Behavior of RRAM Devices for Neuromorphic Applications
Padovani, Andrea;Larcher, Luca;
2017
Abstract
We perform a comparative study of HfO2 and Ta2O5 resistive switching memory (RRAM) devices for their possible application as electronic synapses. By means of electrical characterization and simulations, we link their electrical behavior (digital or analog switching) to the properties and evolution of the conductive filament (CF). More specifically, we identify that bias-polarity-dependent digital switching in HfO2 RRAM is primarily related to the creation and rupture of an oxide barrier. Conversely, the modulation of the CF size in Ta2O5 RRAM allows bias-polarity-independent analog switching with multiple states. Therefore, when the Ta2O5 RRAM is used to implement a synapse in multilayer perceptron neural networks operated by back-propagation algorithms, patterns in handwritten digits can be recognized with high accuracy.File | Dimensione | Formato | |
---|---|---|---|
VQR_Linking_Conductive_Filament_Properties_and_Evolution_to_Synaptic_Behavior_of_RRAM_Devices_for_Neuromorphic_Applications.pdf
Accesso riservato
Tipologia:
Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione
1.53 MB
Formato
Adobe PDF
|
1.53 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
POST_PRINT_LED.2017.2731859.pdf
Open access
Tipologia:
Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione
2.13 MB
Formato
Adobe PDF
|
2.13 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris