An Advanced Memory WRITE Algorithm to Mitigate the Effects of ReRAM Cell Variability

被引:0
作者
Ramirez, Vicente [1 ]
Cayo, Jose [1 ]
Vourkas, Ioarmis [1 ]
机构
[1] Univ Tecn Federico Santa Maria UTFSM, Dept Elect Engn, Valparaiso, Chile
来源
2024 13TH INTERNATIONAL CONFERENCE ON MODERN CIRCUITS AND SYSTEMS TECHNOLOGIES, MOCAST 2024 | 2024年
关键词
memristor; resistive switching; ReRAM; memory access scheme; model; transition fault; simulation; Knowm Inc;
D O I
10.1109/MOCAST61810.2024.10615523
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
ReRAM cells store digital information in form of resistance using low and high resistance states, whose precise distributions are attributed to the inherent switching variability of the devices. Once the SET & RESET threshold values are known, the WRITE pulse amplitudes are selected only slightly larger, avoiding high amplitudes that could impact the device endurance. However, after several cycles such small pulses frequently cause incomplete transitions in WRITE attempts. We exemplify this with experimental measurements on commercial Self-Directed Channel (SDC) memristive devices. To overcome state transition errors, more comprehensive WRITE schemes are required. In this direction, here we discuss the development of an advanced ReRAM WRITE algorithm, as a first approach towards the design of memory control units for ReRAM modules. The proposed driving scheme contemplates gradual and verified WRITE operations, and can successfully cope with the effects of variability. Its effectiveness was validated via high-level simulations in Python, using a behavioral model of memristive devices, which was significantly enriched to support nonideal performance features. The results demonstrate that advanced ReRAM WRITE schemes could mitigate the effects of variability and improve the performance of memory cells.
引用
收藏
页数:4
相关论文
共 17 条
[11]   Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence [J].
Molas, Gabriel ;
Nowak, Etienne .
APPLIED SCIENCES-BASEL, 2021, 11 (23)
[12]   Stochasticity Modeling in Memristors [J].
Naous, Rawan ;
Al-Shedivat, Maruan ;
Salama, Khaled Nabil .
IEEE TRANSACTIONS ON NANOTECHNOLOGY, 2016, 15 (01) :15-28
[13]   All-memristive neuromorphic computing with level-tuned neurons [J].
Pantazi, Angeliki ;
Wozniak, Stanislaw ;
Tuma, Tomas ;
Eleftheriou, Evangelos .
NANOTECHNOLOGY, 2016, 27 (35)
[14]  
Pershin YV, 2013, RADIOENGINEERING, V22, P485
[15]   Variability in Resistive Memories [J].
Roldan, Juan. B. ;
Miranda, Enrique ;
Maldonado, David ;
Mikhaylov, Alexey. N. ;
Agudov, Nikolay., V ;
Dubkov, Alexander. A. ;
Koryazhkina, Maria. N. ;
Gonzalez, Mireia. B. ;
Villena, Marco. A. ;
Poblador, Samuel ;
Saludes-Tapia, Mercedes ;
Picos, Rodrigo ;
Jimenez-Molinos, Francisco ;
Stavrinides, Stavros. G. ;
Salvador, Emili ;
Alonso, Francisco. J. ;
Campabadal, Francesca ;
Spagnolo, Bernardo ;
Lanza, Mario ;
Chua, Leon. O. .
ADVANCED INTELLIGENT SYSTEMS, 2023, 5 (06)
[16]  
WeebitNano, ReRAM Emerging Memory Technology
[17]  
Zhao C., 2021 IEEE INT C ASIC