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Stochastic resonance exploration in current-driven ReRAM devices
- Publication Year :
- 2022
-
Abstract
- © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.<br />Advances in emerging resistive random-access memory (ReRAM) technology show promise for its use in future computing systems, enabling neuromorphic and memory-centric computing architectures. However, one aspect that holds back the widespread practical use of ReRAM is the behavioral variability of resistive switching devices. In this context, a radically new path towards ReRAM-based electronics concerns the exploitation of noise and the Stochastic Resonance (SR) phenomenon as a mechanism to mitigate the impact of variability. While SR has been already demonstrated in ReRAM devices and its potential impact has been analyzed for memory applications, related works have only focused on voltage input signals. In this work we present preliminary results concerning the exploration of SR in current-driven ReRAM devices, commercially available by Knowm Inc. Our results indicate that additive noise of amplitude s = 0.125uA can stabilize the cycling performance of the devices, whereas higher noise amplitude improves the HRS-LRS resistance window, thus could affect positively the Bit Error Rate (BER) metric in ReRAM memory applications.<br />Supported by the Chilean research grants FONDECYT INICIACION 11180706 and ANID-Basal FB0008, and by the Spanish MCIN grants PID2019-105658RB-I00, and MCIN/AEI/10.13039/501100011033 grant PID2019-103869RB-C33.<br />Peer Reviewed<br />Postprint (author's final draft)
Details
- Database :
- OAIster
- Notes :
- 4 p., application/pdf, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1372979596
- Document Type :
- Electronic Resource