Literature DB >> 29086551

Analog Synaptic Behavior of a Silicon Nitride Memristor.

Sungjun Kim1, Hyungjin Kim1, Sungmin Hwang1, Min-Hwi Kim1, Yao-Feng Chang2, Byung-Gook Park1.   

Abstract

In this paper, we present a synapse function using analog resistive-switching behaviors in a SiNx-based memristor with a complementary metal-oxide-semiconductor compatibility and expandability to three-dimensional crossbar array architecture. A progressive conductance change is attainable as a result of the gradual growth and dissolution of the conducting path, and the series resistance of the AlOy layer in the Ni/SiNx/AlOy/TiN memristor device enhances analog switching performance by reducing current overshoot. A continuous and smooth gradual reset switching transition can be observed with a compliance current limit (>100 μA), and is highly suitable for demonstrating synaptic characteristics. Long-term potentiation and long-term depression are obtained by means of identical pulse responses. Moreover, symmetric and linear synaptic behaviors are significantly improved by optimizing pulse response conditions, which is verified by a neural network simulation. Finally, we display the spike-timing-dependent plasticity with the multipulse scheme. This work provides a possible way to mimic biological synapse function for energy-efficient neuromorphic systems by using a conventional passive SiNx layer as an active dielectric.

Entities:  

Keywords:  analog resistive switching; memristor; silicon nitride; spike-timing-dependent plasticity; synapse

Year:  2017        PMID: 29086551     DOI: 10.1021/acsami.7b11191

Source DB:  PubMed          Journal:  ACS Appl Mater Interfaces        ISSN: 1944-8244            Impact factor:   9.229


  7 in total

1.  Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing.

Authors:  Muhammad Umair Khan; Jungmin Kim; Mahesh Y Chougale; Chaudhry Muhammad Furqan; Qazi Muhammad Saqib; Rayyan Ali Shaukat; Nobuhiko P Kobayashi; Baker Mohammad; Jinho Bae; Hoi-Sing Kwok
Journal:  Microsyst Nanoeng       Date:  2022-05-26       Impact factor: 8.006

2.  Improving the Recognition Accuracy of Memristive Neural Networks via Homogenized Analog Type Conductance Quantization.

Authors:  Qilai Chen; Tingting Han; Minghua Tang; Zhang Zhang; Xuejun Zheng; Gang Liu
Journal:  Micromachines (Basel)       Date:  2020-04-18       Impact factor: 2.891

3.  Charge transport mechanism in the forming-free memristor based on silicon nitride.

Authors:  Andrei A Gismatulin; Gennadiy N Kamaev; Vladimir N Kruchinin; Vladimir A Gritsenko; Oleg M Orlov; Albert Chin
Journal:  Sci Rep       Date:  2021-01-28       Impact factor: 4.379

Review 4.  Competing memristors for brain-inspired computing.

Authors:  Seung Ju Kim; Sang Bum Kim; Ho Won Jang
Journal:  iScience       Date:  2020-12-03

5.  Characteristic analysis of volatile avalanche diode threshold switching for bionic nerve synapse applications.

Authors:  Yang Wang; Zeyu Zhong; Xiangliang Jin; Yan Peng; Jun Luo
Journal:  Sci Rep       Date:  2021-10-26       Impact factor: 4.379

6.  Multi-level Cells and Quantized Conductance Characteristics of Al2O3-Based RRAM Device for Neuromorphic System.

Authors:  Yunseok Lee; Jongmin Park; Daewon Chung; Kisong Lee; Sungjun Kim
Journal:  Nanoscale Res Lett       Date:  2022-09-03       Impact factor: 5.418

7.  A Low-Power Spiking Neural Network Chip Based on a Compact LIF Neuron and Binary Exponential Charge Injector Synapse Circuits.

Authors:  Malik Summair Asghar; Saad Arslan; Hyungwon Kim
Journal:  Sensors (Basel)       Date:  2021-06-29       Impact factor: 3.576

  7 in total

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