scholarly journals A TaOx-Based RRAM with Improved Uniformity and Excellent Analog Characteristics by Local Dopant Engineering

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2451
Author(s):  
Yabo Qin ◽  
Zongwei Wang ◽  
Yaotian Ling ◽  
Yimao Cai ◽  
Ru Huang
Keyword(s):  
Neural Network ◽  
Formation Energy ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Collective Effects ◽  
Operating Voltage ◽  
Access Memory ◽  
Intelligent Algorithms ◽  
Network Mapping

Resistive random-access memory (RRAM) with the ability to store and process information has been considered to be one of the most promising emerging devices to emulate synaptic behavior and accelerate the computation of intelligent algorithms. However, variation and limited resistance levels impede RRAM as a synapse for weight storage in neural network mapping. In this work, we investigate a TaOx-based RRAM with Al ion local doping. Compared with a device without doping, the device with locally doped Al ion exhibits excellent uniformity and analog characteristics. The operating voltage and resistance states show tighter distributions. Over 150 adjustable resistance states can be achieved through tuning compliance current (CC) and reset stop voltage. Moreover, incremental resistance switching is available under optimized identical pulses. The improved uniformity and analog characteristics can be attributed to the collective effects of reduced oxygen vacancy (Vo) formation energy and weak conductive filaments induced by the local Al ion dopants.

scholarly journals Three-Dimensional (3D) Vertical Resistive Random-Access Memory (VRRAM) Synapses for Neural Network Systems

Materials ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3451 ◽  
Author(s):  
Wookyung Sun ◽  
Sujin Choi ◽  
Bokyung Kim ◽  
Junhee Park
Keyword(s):  
Neural Network ◽  
Structural Characteristics ◽  
3D Structure ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Operating Principle ◽  
Access Memory ◽  
Network Systems ◽  
Crossbar Array

Memristor devices are generally suitable for incorporation in neuromorphic systems as synapses because they can be integrated into crossbar array circuits with high area efficiency. In the case of a two-dimensional (2D) crossbar array, however, the size of the array is proportional to the neural network’s depth and the number of its input and output nodes. This means that a 2D crossbar array is not suitable for a deep neural network. On the other hand, synapses that use a memristor with a 3D structure are suitable for implementing a neuromorphic chip for a multi-layered neural network. In this study, we propose a new optimization method for machine learning weight changes that considers the structural characteristics of a 3D vertical resistive random-access memory (VRRAM) structure for the first time. The newly proposed synapse operating principle of the 3D VRRAM structure can simplify the complexity of a neuron circuit. This study investigates the operating principle of 3D VRRAM synapses with comb-shaped word lines and demonstrates that the proposed 3D VRRAM structure will be a promising solution for a high-density neural network hardware system.

scholarly journals Comprehensive Examination on Resistive Random Access Memory

2019 ◽  
Vol 8 (4) ◽  
pp. 4663-4667
Keyword(s):  
Materials Science ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Future Research ◽  
Operating Voltage ◽  
Access Memory ◽  
History Of ◽  
Frenkel Emission ◽  
Non Destructive

With the latest advances in materials science, resistive random access memory (RRAM) devices are attracting non-volatile, low power consumption, non-destructive read, and high density memory. Related performance parameters for RRAM devices include operating voltage, operating speed, resistivity, durability, retention time, device yield, and multi-level storage. Numerous resistive mechanisms, such as conductive filaments, space charge limited conduction, trap charging and discharging, Schottky emission, and pool-Frenkel emission, have been proposed to explain the resistance switches of RRAM devices. Therefore, in this work, different oxide-based random access memories (RRAMs) were provided for comprehensive investigation of neuromorphiccalculations. With the development of RRAM, the physical mechanism of conduction, the basic history of neuromorphic calculations begins. Finally, suggestions for future research, as well as waiting for the challenges of RRAM equipment, are given.

3D Integrable W/SiNx/n-Si/p-Si 1D1R Unipolar Resistive Random Access Memory Synapse for Suppressing Reverse Leakage in Spiking Neural Network

2020 ◽  
Vol 20 (8) ◽  
pp. 4735-4739 ◽  
Author(s):  
Chae Soo Kim ◽  
Taehyung Kim ◽  
Kyung Kyu Min ◽  
Sungjun Kim ◽  
Byung-Gook Park
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Current Issue ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Access Memory ◽  
Array Structure

In this paper, we pose reverse leakage current issue which occurs when resistive random access memory (RRAM) is used as synapse for spiking neural networks (SNNs). To prevent this problem, 1 diode-1 RRAM (1D1R) synapse is suggested and simulated to examine their current rectifying chracteristics, Furthermore, high density of 1 K 3D 1D1R synapse array structure and its process flow are proposed.

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.

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