Gradual conductance modulation of Ti/WOx/Pt memristor with self-rectification for a neuromorphic system

2021 ◽  
Vol 119 (1) ◽  
pp. 012102
Author(s):  
Jiwoong Shin ◽  
Myounggon Kang ◽  
Sungjun Kim
Keyword(s):  
Neuromorphic System ◽  
Conductance Modulation

Recent progress on synaptic transistors for neuromorphic system applications

Ceramist ◽  
2018 ◽  
Vol 21 (2) ◽  
pp. 113-114
Author(s):  
Jae Hyeon Nam ◽  
◽  
Hye Yeon Jang ◽  
Tae Hyeon Kim ◽  
Byungjin Cho ◽  
...  
Keyword(s):  
Recent Progress ◽  
Neuromorphic System

Li-memristor based MOSFET synapse for linear I-V characteristic and processing analog input neuromorphic system

2020 ◽  
Author(s):  
Chuljun Lee ◽  
Jaeun Lee ◽  
Myungjun Kim ◽  
Yubin Song ◽  
Geonhui Han ◽  
...  
Keyword(s):  
Analog Input ◽  
Neuromorphic System

Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware

2021 ◽  
Vol 17 (4) ◽  
pp. 1-26
Author(s):  
Md Musabbir Adnan ◽  
Sagarvarma Sayyaparaju ◽  
Samuel D. Brown ◽  
Mst Shamim Ara Shawkat ◽  
Catherine D. Schuman ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Recognition Task ◽  
Single Layer ◽  
Process Variations ◽  
Spike Timing ◽  
System Level ◽  
Design Parameters ◽  
Digital Pulse ◽  
Neuromorphic System ◽  
The Impact

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

scholarly journals Gradually Modified Conductance in the Self-Compliance Region of an Atomic-Layer-Deposited Pt/TiO2/HfAlOx/TiN RRAM Device

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1199
Author(s):  
Hojeong Ryu ◽  
Sungjun Kim
Keyword(s):  
Photoelectron Spectroscopy ◽  
Depth Profiling ◽  
Atomic Layer ◽  
High Resistance State ◽  
Long Term Potentiation ◽  
Memory Device ◽  
Bipolar Resistive Switching ◽  
Term Potentiation ◽  
Resistance State ◽  
Neuromorphic System

This study presents conductance modulation in a Pt/TiO2/HfAlOx/TiN resistive memory device in the compliance region for neuromorphic system applications. First, the chemical and material characteristics of the atomic-layer-deposited films were verified by X-ray photoelectron spectroscopy depth profiling. The low-resistance state was effectively controlled by the compliance current, and the high-resistance state was adjusted by the reset stop voltage. Stable endurance and retention in bipolar resistive switching were achieved. When a compliance current of 1 mA was imposed, only gradual switching was observed in the reset process. Self-compliance was used after an abrupt set transition to achieve a gradual set process. Finally, 10 cycles of long-term potentiation and depression were obtained in the compliance current region for neuromorphic system applications.

scholarly journals Improved Synaptic Device Properties of HfAlOx Dielectric on Highly Doped Silicon Substrate by Partial Reset Process

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 772
Author(s):  
Seunghyun Kim ◽  
Osung Kwon ◽  
Hojeong Ryu ◽  
Sungjun Kim
Keyword(s):  
Resistive Switching ◽  
Silicon Substrate ◽  
Photoelectron Spectroscopy ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Alloy Type ◽  
Switching Type ◽  
Neuromorphic System ◽  
Device Properties ◽  
Partial Reset

This work demonstrates the synaptic properties of the alloy-type resistive random-access memory (RRAM). We fabricated the HfAlOx-based RRAM for a synaptic device in a neuromorphic system. The deposition of the HfAlOx film on the silicon substrate was verified by X-ray photoelectron spectroscopy (XPS) analysis. It was found that both abrupt and gradual resistive switching could be implemented, depending on the reset stop voltage. In the reset process, the current gradually decreased at weak voltage, and at strong voltage, it tended to decrease rapidly by Joule heating. The type of switching determined by the first reset process was subsequently demonstrated to be stable switching by successive set and reset processes. A gradual switching type has a much smaller on/off window than abrupt switching. In addition, retention maintained stability up to 2000 s in both switching cases. Next, the multiple current states were tested in the gradual switching case by identical pulses. Finally, we demonstrated the potentiation and depression of the Cu/HfAlOx/Si device as a synapse in an artificial neural network and confirmed that gradual resistive switching was suitable for artificial synapses, using neuromorphic system simulation.

scholarly journals A Comparison Study on Multilayered Barrier Oxide Structure in Charge Trap Flash for Synaptic Operation

Crystals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Minkyung Kim ◽  
Eunpyo Park ◽  
In Soo Kim ◽  
Jongkil Park ◽  
Jaewook Kim ◽  
...  
Keyword(s):  
Memory Device ◽  
Spike Timing ◽  
Comparison Study ◽  
Oxide Structure ◽  
Charge Trap ◽  
Synaptic Functions ◽  
Essential Components ◽  
Neuromorphic System ◽  
A Charge ◽  
Synaptic Operation

A synaptic device that contains weight information between two neurons is one of the essential components in a neuromorphic system, which needs highly linear and symmetric characteristics of weight update. In this study, a charge trap flash (CTF) memory device with a multilayered high-κ barrier oxide structure on the MoS2 channel is proposed. The fabricated device was oxide-engineered on the barrier oxide layers to achieve improved synaptic functions. A comparison study between two fabricated devices with different barrier oxide materials (Al2O3 and SiO2) suggests that a high-κ barrier oxide structure improves the synaptic operations by demonstrating the increased on/off ratio and symmetry of synaptic weight updates due to a better coupling ratio. Lastly, the fabricated device has demonstrated reliable potentiation and depression behaviors and spike-timing-dependent plasticity (STDP) for use in a spiking neural network (SNN) neuromorphic system.

Aharonov-Bohm Conductance Modulation in Ballistic Carbon Nanotubes

2007 ◽  
Vol 98 (17) ◽  
Author(s):  
B. Lassagne ◽  
J-P. Cleuziou ◽  
S. Nanot ◽  
W. Escoffier ◽  
R. Avriller ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Aharonov Bohm ◽  
Conductance Modulation

Conductance Modulation in Double Quantum Wells Due to Magnetic Field-Induced Anticrossing

1994 ◽  
Vol 73 (16) ◽  
pp. 2256-2259 ◽  
Author(s):  
J. A. Simmons ◽  
S. K. Lyo ◽  
N. E. Harff ◽  
J. F. Klem
Keyword(s):  
Magnetic Field ◽  
Quantum Wells ◽  
Double Quantum ◽  
Conductance Modulation
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