Nanomaterials

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9 pages, 4888 KiB

Open AccessArticle

Sol–Gel-Processed Y₂O₃–Al₂O₃ Mixed Oxide-Based Resistive Random-Access-Memory Devices

by Hae-In Kim, Taehun Lee, Yoonjin Cho, Sangwoo Lee, Won-Yong Lee, Kwangeun Kim and Jaewon Jang

Nanomaterials 2023, 13(17), 2462; https://doi.org/10.3390/nano13172462 - 31 Aug 2023

Viewed by 985

Abstract

Herein, sol–gel-processed Y₂O₃–Al₂O₃ mixed oxide-based resistive random-access-memory (RRAM) devices with different proportions of the involved Y₂O₃ and Al₂O₃ precursors were fabricated on indium tin oxide/glass substrates. The corresponding structural, chemical, [...] Read more.

Herein, sol–gel-processed Y₂O₃–Al₂O₃ mixed oxide-based resistive random-access-memory (RRAM) devices with different proportions of the involved Y₂O₃ and Al₂O₃ precursors were fabricated on indium tin oxide/glass substrates. The corresponding structural, chemical, and electrical properties were investigated. The fabricated devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. With an increase in the percentage of Al₂O₃ precursor above 50 mol%, the crystallinity reduced, with the amorphous phase increasing owing to internal stress. Moreover, with increasing Al₂O₃ percentage, the lattice oxygen percentage increased and the oxygen vacancy percentage decreased. A 50% Y₂O₃–50% Al₂O₃ mixed oxide-based RRAM device exhibited the maximum high-resistance-state/low-resistance-state (HRS/LRS) ratio, as required for a large readout margin and array size. Additionally, this device demonstrated good endurance characteristics, maintaining stability for approximately 100 cycles with a high HRS/LRS ratio (>10⁴). The HRS and LRS resistances were also retained up to 10⁴ s without considerable degradation. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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10 pages, 2807 KiB

Open AccessEditor’s ChoiceArticle

Sol–Gel-Processed Y₂O₃ Multilevel Resistive Random-Access Memory Cells for Neural Networks

by Taehun Lee, Hae-In Kim, Yoonjin Cho, Sangwoo Lee, Won-Yong Lee, Jin-Hyuk Bae, In-Man Kang, Kwangeun Kim, Sin-Hyung Lee and Jaewon Jang

Nanomaterials 2023, 13(17), 2432; https://doi.org/10.3390/nano13172432 - 27 Aug 2023

Cited by 1 | Viewed by 1075

Abstract

Yttrium oxide (Y₂O₃) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the [...] Read more.

Yttrium oxide (Y₂O₃) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the Y₂O₃ RRAM devices was investigated, and the results revealed that the resistance values gradually decreased with increasing set current compliance values. By regulating these values, the formation of pure Ag conductive filament could be restricted. The dominant oxygen ion diffusion and migration within Y₂O₃ leads to the formation of oxygen vacancies and Ag metal-mixed conductive filaments between the two electrodes. The filament composition changes from pure Ag metal to Ag metal mixed with oxygen vacancies, which is crucial for realizing multilevel cell (MLC) switching. Consequently, intermediate resistance values were obtained, which were suitable for MLC switching. The fabricated Y₂O₃ RRAM devices could function as a MLC with a capacity of two bits in one cell, utilizing three low-resistance states and one common high-resistance state. The potential of the Y₂O₃ RRAM devices for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y₂O₃ RRAM devices demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This indicates that the proposed RRAM can be utilized as a memory component in practical neuromorphic systems. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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12 pages, 9853 KiB

Open AccessArticle

Multi-Level Resistive Al/Ga₂O₃/ITO Switching Devices with Interlayers of Graphene Oxide for Neuromorphic Computing

by Li-Wen Wang, Chih-Wei Huang, Ke-Jing Lee, Sheng-Yuan Chu and Yeong-Her Wang

Nanomaterials 2023, 13(12), 1851; https://doi.org/10.3390/nano13121851 - 13 Jun 2023

Cited by 3 | Viewed by 1483

Abstract

Recently, resistive random access memory (RRAM) has been an outstanding candidate among various emerging nonvolatile memories for high-density storage and in-memory computing applications. However, traditional RRAM, which accommodates two states depending on applied voltage, cannot meet the high density requirement in the era [...] Read more.

Recently, resistive random access memory (RRAM) has been an outstanding candidate among various emerging nonvolatile memories for high-density storage and in-memory computing applications. However, traditional RRAM, which accommodates two states depending on applied voltage, cannot meet the high density requirement in the era of big data. Many research groups have demonstrated that RRAM possesses the potential for multi-level cells, which would overcome demands related to mass storage. Among numerous semiconductor materials, gallium oxide (a fourth-generation semiconductor material) is applied in the fields of optoelectronics, high-power resistive switching devices, and so on, due to its excellent transparent material properties and wide bandgap. In this study, we successfully demonstrate that Al/graphene oxide (GO)/Ga₂O₃/ITO RRAM has the potential to achieve two-bit storage. Compared to its single-layer counterpart, the bilayer structure has excellent electrical properties and stable reliability. The endurance characteristics could be enhanced above 100 switching cycles with an ON/OFF ratio of over 10³. Moreover, the filament models are also described in this thesis to clarify the transport mechanisms. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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10 pages, 2611 KiB

Open AccessArticle

Characteristics of Synaptic Function of Mesoporous Silica–Titania and Mesoporous Titania Lateral Electrode Devices

by Dhanashri Vitthal Desai, Jongmin Yang and Hyun Ho Lee

Nanomaterials 2023, 13(11), 1734; https://doi.org/10.3390/nano13111734 - 25 May 2023

Cited by 1 | Viewed by 1071

Abstract

In this paper, we have fabricated non-volatile memory resistive switching (RS) devices and analyzed analog memristive characteristics using lateral electrodes with mesoporous silica–titania (meso-ST) and mesoporous titania (meso-T) layers. For the planar-type device having two parallel electrodes, current–voltage (I–V) curves and pulse-driven current [...] Read more.

In this paper, we have fabricated non-volatile memory resistive switching (RS) devices and analyzed analog memristive characteristics using lateral electrodes with mesoporous silica–titania (meso-ST) and mesoporous titania (meso-T) layers. For the planar-type device having two parallel electrodes, current–voltage (I–V) curves and pulse-driven current changes could reveal successful long-term potentiation (LTP) along with long-term depression (LTD), respectively, by the RS active mesoporous two layers for 20~100 μm length. Through mechanism characterization using chemical analysis, non-filamental memristive behavior unlike the conventional metal electroforming was identified. Additionally, high performance of the synaptic operations could be also accomplished such that a high current of 10⁻⁶ Amp level could occur despite a wide electrode spacing and short pulse spike biases under ambient condition with moderate humidity (RH 30~50%). Moreover, it was confirmed that rectifying characteristics were observed during the I–V measurement, which was a representative feature of dual functionality of selection diode and the analog RS device for both meso-ST and meso-T devices. The memristive and synaptic functions along with the rectification property could facilitate a chance of potential implementation of the meso-ST and meso-T devices to neuromorphic electronics platform. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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22 pages, 13752 KiB

Open AccessFeature PaperArticle

Intense pH Sensitivity Modulation in Carbon Nanotube-Based Field-Effect Transistor by Non-Covalent Polyfluorene Functionalization

by Gookbin Cho, Eva Grinenval, Jean-Christophe P. Gabriel and Bérengère Lebental

Nanomaterials 2023, 13(7), 1157; https://doi.org/10.3390/nano13071157 - 24 Mar 2023

Viewed by 1531

Abstract

We compare the pH sensing performance of non-functionalized carbon nanotubes (CNT) field-effect transistors (p-CNTFET) and CNTFET functionalized with a conjugated polyfluorene polymer (labeled FF-UR) bearing urea-based moieties (f-CNTFET). The devices are electrolyte-gated, PMMA-passivated, 5 µm-channel FETs with unsorted, inkjet-printed single-walled CNT. In phosphate [...] Read more.

We compare the pH sensing performance of non-functionalized carbon nanotubes (CNT) field-effect transistors (p-CNTFET) and CNTFET functionalized with a conjugated polyfluorene polymer (labeled FF-UR) bearing urea-based moieties (f-CNTFET). The devices are electrolyte-gated, PMMA-passivated, 5 µm-channel FETs with unsorted, inkjet-printed single-walled CNT. In phosphate (PBS) and borate (BBS) buffer solutions, the p-CNTFETs exhibit a p-type operation while f-CNTFETs exhibit p-type behavior in BBS and ambipolarity in PBS. The sensitivity to pH is evaluated by measuring the drain current at a gate and drain voltage of −0.8 V. In PBS, p-CNTFETs show a linear, reversible pH response between pH 3 and pH 9 with a sensitivity of 26 ± 2.2%/pH unit; while f-CNTFETs have a much stronger, reversible pH response (373%/pH unit), but only over the range of pH 7 to pH 9. In BBS, both p-CNTFET and f-CNTFET show a linear pH response between pH 5 and 9, with sensitivities of 56%/pH and 96%/pH, respectively. Analysis of the I–V curves as a function of pH suggests that the increased pH sensitivity of f-CNTFET is consistent with interactions of FF-UR with phosphate ions in PBS and boric acid in BBS, with the ratio and charge of the complexed species depending on pH. The complexation affects the efficiency of electrolyte gating and the surface charge around the CNT, both of which modify the I–V response of the CNTFET, leading to the observed current sensitivity as a function of pH. The performances of p-CNTFET in PBS are comparable to the best results in the literature, while the performances of the f-CNTFET far exceed the current state-of-the-art by a factor of four in BBS and more than 10 over a limited range of pH in BBS. This is the first time that a functionalization other than carboxylate moieties has significantly improved the state-of-the-art of pH sensing with CNTFET or CNT chemistors. On the other hand, this study also highlights the challenge of transferring this performance to a real water matrix, where many different species may compete for interactions with FF-UR. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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12 pages, 3928 KiB

Open AccessArticle

Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications

by Jianmin Zeng, Xinhui Chen, Shuzhi Liu, Qilai Chen and Gang Liu

Nanomaterials 2023, 13(5), 803; https://doi.org/10.3390/nano13050803 - 22 Feb 2023

Cited by 5 | Viewed by 2153

Abstract

Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, [...] Read more.

Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO₄)]₂/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device’s conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device’s synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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10 pages, 2351 KiB

Open AccessCommunication

A “Special” Solvent to Prepare Alloyed Pd₂Ni₁ Nanoclusters on a MWCNT Catalyst for Enhanced Electrocatalytic Oxidation of Formic Acid

by Pingping Yang, Li Zhang, Xuejiao Wei, Shiming Dong, Wenting Cao, Dong Ma, Yuejun Ouyang, Yixi Xie and Junjie Fei

Nanomaterials 2023, 13(4), 755; https://doi.org/10.3390/nano13040755 - 17 Feb 2023

Cited by 2 | Viewed by 1293

Abstract

Herein, an electrocatalyst with Pd₂Ni₁ nanoclusters, supporting multiwalled carbon nanotubes (MWCNTs) (referred to Pd₂Ni₁/CNTs), was fabricated with deep eutectic solvents (DES), which simultaneously served as reducing agent, dispersant, and solvent. The mass activity of the catalyst [...] Read more.

Herein, an electrocatalyst with Pd₂Ni₁ nanoclusters, supporting multiwalled carbon nanotubes (MWCNTs) (referred to Pd₂Ni₁/CNTs), was fabricated with deep eutectic solvents (DES), which simultaneously served as reducing agent, dispersant, and solvent. The mass activity of the catalyst for formic acid oxidation reaction (FAOR) was increased nearly four times compared to a Pd/C catalyst. The excellent catalytic activity of Pd₂Ni₁/CNTs was ascribed to the special nanocluster structure and appropriate Ni doping, which changed the electron configuration of Pd to reduce the d-band and to produce a Pd–Ni bond as a new active sites. These newly added Ni sites obtained more OH⁻ to release more effective active sites by interacting with the intermediate produced in the first step of FAOR. Hence, this study provides a new method for preparing a Pd–Ni catalyst with high catalytic performance. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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15 pages, 5546 KiB

Open AccessArticle

Reduced Graphene Oxide UWB Array Sensor: High Performance for Brain Tumor Imaging and Detection

by Mohd Aminudin Jamlos, Mohd Faizal Jamlos, Wan Azani Mustafa, Nur Amirah Othman, Mohamad Nur Khairul Hafizi Rohani, Syahrul Affandi Saidi, Mohd Sharizan Md Sarip and Mohd Al Hafiz Mohd Nawi

Nanomaterials 2023, 13(1), 27; https://doi.org/10.3390/nano13010027 - 21 Dec 2022

Cited by 3 | Viewed by 1096

Abstract

A low cost, with high performance, reduced graphene oxide (RGO) Ultra-wide Band (UWB) array sensor is presented to be applied with a technique of confocal radar-based microwave imaging to recognize a tumor in a human brain. RGO is used to form its patches [...] Read more.

A low cost, with high performance, reduced graphene oxide (RGO) Ultra-wide Band (UWB) array sensor is presented to be applied with a technique of confocal radar-based microwave imaging to recognize a tumor in a human brain. RGO is used to form its patches on a Taconic substrate. The sensor functioned in a range of 1.2 to 10.8 GHz under UWB frequency. The sensor demonstrates high gain of 5.2 to 14.5 dB, with the small size of 90 mm × 45 mm², which can be easily integrated into microwave imaging systems and allow the best functionality. Moreover, the novel UWB RGO array sensor is established as a detector with a phantom of the human head. The layers’ structure represents liquid-imitating tissues that consist of skin, fat, skull, and brain. The sensor will scan nine different points to cover the whole one-sided head phantom to obtain equally distributed reflected signals under two different situations, namely the existence and absence of the tumor. In order to accurately detect the tumor by producing sharper and clearer microwave image, the Matrix Laboratory software is used to improve the microwave imaging algorithm (delay and sum) including summing the imaging algorithm and recording the scattering parameters. The existence of a tumor will produce images with an error that is lower than 2 cm. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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10 pages, 1876 KiB

Open AccessArticle

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)_x(LiNbO₃)_100−x Nanocomposite Memristors

by Anna N. Matsukatova, Aleksandr I. Iliasov, Kristina E. Nikiruy, Elena V. Kukueva, Aleksandr L. Vasiliev, Boris V. Goncharov, Aleksandr V. Sitnikov, Maxim L. Zanaveskin, Aleksandr S. Bugaev, Vyacheslav A. Demin, Vladimir V. Rylkov and Andrey V. Emelyanov

Nanomaterials 2022, 12(19), 3455; https://doi.org/10.3390/nano12193455 - 3 Oct 2022

Cited by 12 | Viewed by 1887

Abstract

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use [...] Read more.

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)_x(LiNbO₃)_100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements. Full article

(This article belongs to the Special Issue Nanostructures for Integrated Devices)

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