scholarly journals A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks

2018 ◽  
Vol 1 (1) ◽  
pp. 75-114 ◽  
Author(s):  
Sparsh Mittal
Keyword(s):  
Machine Learning ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Low Energy ◽  
Power Budget ◽  
Access Memory ◽  
Computing Systems ◽  
Data Movement ◽  
Similarities And Differences

As data movement operations and power-budget become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as processing-in-memory (PIM), machine learning (ML), and especially neural network (NN)-based accelerators has grown significantly. Resistive random access memory (ReRAM) is a promising technology for efficiently architecting PIM- and NN-based accelerators due to its capabilities to work as both: High-density/low-energy storage and in-memory computation/search engine. In this paper, we present a survey of techniques for designing ReRAM-based PIM and NN architectures. By classifying the techniques based on key parameters, we underscore their similarities and differences. This paper will be valuable for computer architects, chip designers and researchers in the area of machine learning.

Advances in RRAM Technology: Identifying and Mitigating Roadblocks

2016 ◽  
Vol 25 (01n02) ◽  
pp. 1640006 ◽  
Author(s):  
D. Veksler ◽  
G. Bersuker
Keyword(s):  
Material Properties ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Operating Speed ◽  
Physical Processes ◽  
Access Memory ◽  
Computing Systems ◽  
Operation Conditions ◽  
Power Operation

Superior scalability, endurance, low power operation, retention, and operating speed of filamentary crystalline HfOx-based resistive random access memory (RRAM) makes this technology promising for implementation in exascale neuromorphic computing systems. Challenges, roadblocks for the implementation, and possible resolutions are discussed. Technological solutions to overcome RRAM variability (both device-to-device and cycle-to-cycle) and read instability are discussed. Major material properties and operation conditions controlling performance of the crystalline HfOx-based RRAM devices are linked to physical processes determining RRAM characteristics.

scholarly journals Low-energy Resistive Random Access Memory Devices with No Need for a Compliance Current

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Zedong Xu ◽  
Lina Yu ◽  
Yong Wu ◽  
Chang Dong ◽  
Ning Deng ◽  
...  
Keyword(s):  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Low Energy ◽  
Memory Devices ◽  
Access Memory

scholarly journals High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Kena Zhang ◽  
Jianjun Wang ◽  
Yuhui Huang ◽  
Long-Qing Chen ◽  
P. Ganesh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resistive Switching ◽  
High Throughput ◽  
Switching Time ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Analytical Models ◽  
Access Memory ◽  
Material Parameters

AbstractMetal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a phase-field model is developed, based on materials properties determined by ab initio calculations, to investigate the role of electrical bias, heat transport and defect-induced Vegard strain in the resistive switching behavior, using MO2−x systems such as HfO2−x as a prototypical model system. It successfully captures the CF formation and resultant bipolar resistive switching characteristics. High-throughput simulations are performed for RRAMs with different material parameters to establish a dataset, based on which a compressed-sensing machine learning is conducted to derive interpretable analytical models for device performance (current on/off ratio and switching time) metrics in terms of key material parameters (electrical and thermal conductivities, Vegard strain coefficients). These analytical models reveal that optimal performance (i.e., high current on/off ratio and low switching time) can be achieved in materials with a low Lorenz number, a fundamental material constant. This work provides a fundamental understanding to the resistive switching in RRAM and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance, which can also be applied to other electro-thermo-mechanical systems.

Low‐Energy Oxygen Plasma Injection of 2D Bi 2 Se 3 Realizes Highly Controllable Resistive Random Access Memory

2021 ◽  
pp. 2108455
Author(s):  
Chujun Yin ◽  
Chuanhui Gong ◽  
Siying Tian ◽  
Yi Cui ◽  
Xuepeng Wang ◽  
...  
Keyword(s):  
Oxygen Plasma ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Low Energy ◽  
Access Memory ◽  
Plasma Injection

Effect of Conductive Filament Temperature on ZrO2 based Resistive Random Access Memory Devices

2020 ◽  
Vol 12 (2) ◽  
pp. 02008-1-02008-4
Author(s):  
Pramod J. Patil ◽  
◽  
Namita A. Ahir ◽  
Suhas Yadav ◽  
Chetan C. Revadekar ◽  
...  
Keyword(s):  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Memory Devices ◽  
Access Memory ◽  
Conductive Filament ◽  
Filament Temperature

scholarly journals Improved Device Distribution in High-Performance SiNx Resistive Random Access Memory via Arsenic Ion Implantation

Nanomaterials ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1401
Author(s):  
Te Jui Yen ◽  
Albert Chin ◽  
Vladimir Gritsenko
Keyword(s):  
High Performance ◽  
Conduction Mechanism ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Switching Behavior ◽  
Access Memory ◽  
Current Voltage ◽  
Current Voltage Characteristics ◽  
Limited Conduction

Large device variation is a fundamental challenge for resistive random access memory (RRAM) array circuit. Improved device-to-device distributions of set and reset voltages in a SiNx RRAM device is realized via arsenic ion (As+) implantation. Besides, the As+-implanted SiNx RRAM device exhibits much tighter cycle-to-cycle distribution than the nonimplanted device. The As+-implanted SiNx device further exhibits excellent performance, which shows high stability and a large 1.73 × 103 resistance window at 85 °C retention for 104 s, and a large 103 resistance window after 105 cycles of the pulsed endurance test. The current–voltage characteristics of high- and low-resistance states were both analyzed as space-charge-limited conduction mechanism. From the simulated defect distribution in the SiNx layer, a microscopic model was established, and the formation and rupture of defect-conductive paths were proposed for the resistance switching behavior. Therefore, the reason for such high device performance can be attributed to the sufficient defects created by As+ implantation that leads to low forming and operation power.

First-principles study of resistive random access memory based on single-layer black phosphorous resistive layer

2020 ◽  
Vol 128 (21) ◽  
pp. 215702
Author(s):  
Yuehua Dai ◽  
Jianhua Gao ◽  
Lihua Huang ◽  
Renjie Ding ◽  
Peng Wang ◽  
...  
Keyword(s):  
First Principles ◽  
Random Access ◽  
Single Layer ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Access Memory ◽  
Resistive Layer ◽  
First Principles Study ◽  
Black Phosphorous

Polymer-based non-volatile resistive random-access memory device fabrication with multi-level switching and negative differential resistance state

2021 ◽  
pp. 106228
Author(s):  
Sobia Ali Khan ◽  
Mehr Khalid Rahmani ◽  
HyungWon Kim ◽  
Muhammad Farooq Khan ◽  
Changhun Yun ◽  
...  
Keyword(s):  
Negative Differential Resistance ◽  
Random Access ◽  
Random Access Memory ◽  
Differential Resistance ◽  
Memory Device ◽  
Resistive Random Access Memory ◽  
Device Fabrication ◽  
Access Memory ◽  
Resistance State ◽  
Multi Level

Multilevel oxygen-vacancy conductive filaments in β-Ga2O3 based resistive random access memory

2021 ◽  
Vol 23 (10) ◽  
pp. 5975-5983
Author(s):  
Jie Hou ◽  
Rui Guo ◽  
Jie Su ◽  
Yawei Du ◽  
Zhenhua Lin ◽  
...  
Keyword(s):  
Oxygen Vacancy ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Access Memory ◽  
Low Resistance ◽  
Multilevel Storage

In this study, at least three kinds of VOs and conductive filaments with low resistance states and forming and set voltages are found for β-Ga2O3 memory. This suggests the great potential of β-Ga2O3 memory for multilevel storage application.

Sign in / Sign up

Export Citation Format

Share Document