Query:  of a Silicon Nitride Memristor. . Mater

Candidate papers:

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 mA) 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.
author_names: Sungjun Kim

Hyungjin Kim

Sungmin Hwang

Min-Hwi Kim

Yao-Feng Chang

Byung-Gook Park
title: Analog Synaptic Behavior of a Silicon Nitride Memristor.
venue: ACS applied materials interfaces
year: 2017

abstract: We demonstrate the hardware implementation of spiking neural network (SNN) with synaptic transistors and neuron circuits. The method of conversion from software fully connected network (FCN) to hardware SNN with little degradation is discussed. The degradation of classification accuracy is analyzed in terms of device variation and noisy images. In addition, the accuracy degradation is significantly improved by stacking denoising autoencoder (DAE) layer. FCN SNN conversion with very little performance drop is demonstrated using weight normalization, and SNN with DAE layer shows a great tolerance to input image noise.
author_names: Hyungjin Kim

Sungmin Hwang

Jung Jin Park

Sangdoo Yun

Jongho Lee

Byung-Gook Park
title: Spiking Neural Network Using Synaptic Transistors and Neuron Circuits for Pattern Recognition With Noisy Images
venue: IEEE Electron Device Letters
year: 2018

abstract: Memristors attract wide attention due to its high integration and parallel computation, having great potential to promote the development of machine learning. As memristors are prone to internal and external variabilities, their variabilities hurt the performance of memristors and, therefore, the performance of memristive neural networks. In this paper, the influence of memristors' stability on machine learning is analyzed. Based on a filamentary memristors' compact circuit model, two typical machine learning methods, a feed forward network and a data clustering, as the representatives of supervised and unsupervised learnings, are tested, following the model's four variation parameters, the variations of maximum memristances, of conductive filaments' change speeds, of initial conductive filaments' lengths, and of minimum memristances. Results show that in a feed forward network, the changing speeds of conductive filaments' length play a key role. What is more, the smaller feed forward network tends to worse performance. In data clustering, the variations of the maximum and minimum memristances have a determinant effect on performance. While the variations of conductive filaments' change, speeds, and initial conductive filaments' lengths show random influence. Moreover, the migration trend of clustering centers does not change with the size of neural networks. We hope the exploration in this paper can deepen the understanding of memristor's role in machine learning and give guidelines for the design and fabrication of memristive neural networks.
author_names: Ruohua Zhu

Shizhuo Ye

Zhiri Tang

Peng Lin

Qijun Huang

Hao Wang

Jin He

Sheng Chang
title: Influence of Compact Memristors' Stability on Machine Learning
venue: IEEE Access
year: 2019

abstract: We report charge storage based memristive operation of a junctionless thin film flash memory cell when it is operated as a two terminal device by grounding the gate. Unlike memristors based on nanoionics, the presented device mode, which we refer to as the flashristor mode, potentially allows greater control over the memristive properties, allowing rational design. The mode is demonstrated using a depletion type n channel ZnO transistor grown by atomic layer deposition (ALD) with HfO2 as the tunnel dielectric, Al2O3 as the control dielectric, and non stoichiometric silicon nitride as the charge storage layer. The device exhibits the pinched hysteresis of a memristor and in the unoptimized device, Roff/Ron ratios of about 3 are presented with low operating voltages below 5 V. A simplified model predicts Roff/Ron ratios can be improved significantly by adjusting the native threshold voltage of the devices. The repeatability of the resistive switching is excellent and devices exhibit 106 s retention time, w.
author_names: Ikram Orak

Mustafa Urel

Gokhan Bakan

Aykutlu Dana
title: Memristive behavior in a junctionless flash memory cell
year: 2015

abstract: Resistive switching in amorphous silicon carbide (a SiC) films deposited by a single composite target magnetron sputtering process is reported. Switching performance as a function of thickness of the films (50, 100 and 300 nm) as well as different top metal electrodes (Cu, Pt and Ag) with the bottom electrode fixed as Au, is investigated. The switching parameters (Forming Voltage, Set and Reset voltages and corresponding currents) are found to be dependent on thickness of SiC films and it is observed that 100 nm is the optimal thickness for best endurance. The interface between metal electrode and a SiC films plays a more significant role in achieving switching performance. Resistance Off/On ratios of 108, retention times >104 s and endurance of 50 cycles are achieved in the best devices. Cross sectional scanning electron microscopy provides evidence that the mechanism of switching involves the formation of carbonaceous filaments and Raman spectroscopy indicates that these filaments are nanocrystalline graphite in nature. The current work clearly establishes that there is dissociation of SiC during the switching cycles leading to formation of nanocrystalline graphitic filaments. These contribute to switching, in addition to the metallic filaments, in the a SiC based resistive memory device.
author_names: Poreddy Chaitanya Akshara

Nilanjana Basu

Jayeeta Lahiri

Mamidipudi Ghanashyam Krishna
title: The role of graphitic filaments in resistive switching behaviour of amorphous silicon carbide thin films.
year: 2019

abstract: Ion conducting memristors comprised of the layered materials Ge2Se3/SnSe/Ag are promising candidates for neuromorphic computing applications. Here, the spike timing dependent plasticity (STDP) application is demonstrated for the first time with a single memristor type operating as a synapse over a timescale of 10 orders of magnitude, from nanoseconds through seconds. This large dynamic range allows the memristors to be useful in applications that require slow biological times, as well as fast times such as needed in neuromorphic computing, thus allowing multiple functions in one design for one memristor type a "one size fits all" approach. This work also investigated the effects of varying the spike pulse shapes on the STDP response of the memristors. These results showed that small changes in the pre and postsynaptic pulse shape can have a significant impact on the STDP. These results may provide circuit designers with insights into how pulse shape affects the actual memristor STDP response and aid them in the design of neuromorphic circuits and systems that can take advantage of certain features in the memristor STDP response that are programmable via the pre and postsynaptic pulse shapes. In addition, the energy requirement per memristor is approximated based on the pulse shape and timing responses. The energy requirement estimated per memristor operating on slower biological timescales (milliseconds to seconds) is larger (nanojoules range) as expected, than the faster (nanoseconds) operating times ~0.1 pJ in some cases) Lastly, the memristors responded in a similar manner under normal STDP conditions (pre and post spikes applied to opposite memristor terminals) as they did to the case where a waveform corresponding to the difference between pre and post spikes was applied to only one electrode, with the other electrode held at ground potential. By applying the difference signal to only one terminal, testing of the memristor in various applications can be achieved with a simplified test set up, and thus be easier to accomplish in most laboratories.
author_names: Kristy A Campbell

Kolton T Drake

Elisa H Barney Smith
title: Pulse Shape and Timing Dependence on the Spike Timing Dependent Plasticity Response of Ion Conducting Memristors as Synapses
venue: Front. Bioeng. Biotechnol.
year: 2016

abstract: A neuromorphic electronic system requires the component devices to not only mimic typical synaptic behaviors but also be energy efficient, together with excellent uniformity and tunable memory time. For this purpose, we fabricated amorphous In Ga Zn O (a IGZO) thin film transistors with plasma enhanced atomic layer deposition AlOx:H dielectrics, successfully demonstrating typical synaptic behaviors, such as excitatory and inhibitory postsynaptic current, pair pules facilitation, dynamic filter, learning and forgetting abilities and spike timing dependent plasticity. In particular, such synaptic transistors exhibit ultralow energy consumption down to 3.18 fJ per synaptic event and tunable extensive memory time ranging from 76.6 ms to at least thousands of seconds. The ultralow energy consumption is realized by electron trapping and releasing at and near the interface between a IGZO channel and AlOx:H dielectric under low voltages. By adjusting the concentration of oxygen vacancy defects in the a IGZO domain adjacent to the interface by means of changing the growth temperature of the AlOx:H dielectrics, the memory time of the device can be further tuned on a large scale. Device flexibility was also demonstrated by fabricating the synaptic transistors onto polymer substrates at room temperature.
author_names: Lingkai Li

Yan Shao

Xiaolin Wang

Xiaohan Wu

Wenjun Liu

David-Wei Zhang

S J Ding
title: Flexible Femtojoule Energy Consumption In Ga Zn O Synaptic Transistors With Extensively Tunable Memory Time
venue: IEEE Transactions on Electron Devices
year: 2020

abstract: The power consumption (PC) window margin (WM) thermal stability (TS) and reliability [i.e. endurance (EN) and retention time (RT) of aluminum nitride (AlN) resistive random access memory (RRAM) devices with different film orientations [i.e. amorphous, (100) and (002) oriented] are investigated. It is shown that the charge carrier transport mechanism of the three devices is dominated by Ohmic behavior under low electric fields and the trap controlled space charge limited current (TC SCLC) model under high electric fields. Among the three devices, the (002) oriented AlN sample provides the best overall PC, WM, TS, EN, and RT performance due to its {c} axis orientation, which is favorable for the formation and rupture of the vertically arranged conductive filaments (CFs) in the AlN film.
author_names: Huei Yu Liou

Pei-Hao Hung

Sheng-Yuan Chu

Chih-Yu Huang

Cheng-Shong Hong
title: Effects of Film Orientation on Power Consumption, Thermal Stability, and Reliability of Aluminum Nitride Resistive Random Access Memory Devices
venue: IEEE Transactions on Electron Devices
year: 2019

abstract: Artificial neurons and synapses are critical units for processing intricate information in brain inspired neuromorphic systems. Memristors are frequently engineered as artificial synapses due to their simple structures, nonlinear dynamics, and high density integration. However, the development of artificial neurons on memristors has less progress. In this letter, we propose a rich dynamics driven artificial neuron based on two dimensional materials MXene. Partial essential neural features of neural processing, including leaky integration, automatic threshold driven fire, and self recovery, were successfully emulated in a unified manner. The space charge limited current (SCLC) model accompanied by electrochemical metallization effect was used to explain electrical characteristics. This work will provide a useful guideline for designing and manipulating memristor as artificial neurons for brain inspired systems.
author_names: Xinwei Liu

Er-Tao Hu

Subhranu Samanta

Xiaojuan Lian
title: Realization of Artificial Neuron Using MXene Bi Directional Threshold Switching Memristors
venue: IEEE Electron Device Letters
year: 2019

abstract: In this letter, biologic synaptic functions of organic inorganic hybrid memristors using novel Ti based maleic acid (Ti MA)/TiO2 ultrathin films have been demonstrated. The memristive functional layer consists of 4 nm Ti MA and 4 nm TiO2, which was fabricated at 160deg C by molecular layer deposition (MLD)/atomic layer deposition (ALD) respectively. A typical bipolar resistive switching characteristic has been confirmed in the TaN/Ti MA/TiO2/Pt bilayer memristors with the space charge limited current model. Using different electrical pulse modules, several important biologic synaptic functions such as nonlinear transmission characteristics, short /long term plasticity, paired pulse facilitation, and spike timing dependent plasticity have been achieved. The organic inorganic hybrid memristors of ultrathin bilayer Ti MA/TiO2 derived by low temperature MLD/ALD are attractive for neuromorphic simulation and flexible neuroscience applications.
author_names: Chang Liu

Yanqiang Cao

Di Wu

Aidong Li
title: Simulation of Biologic Synapse Through Organic Inorganic Hybrid Memristors Using Novel Ti Based Maleic Acid/TiO2 Ultrathin Films
venue: IEEE Electron Device Letters
year: 2020