Multilevel resistive switching in hydrothermally synthesized FeWO4 thin film-based memristive device for non-volatile memory application.

The memristors offer significant advantages as a key element in non-volatile and brain-inspired neuromorphic systems because of their salient features such as remarkable endurance, ability to store multiple bits, fast operation speed, and extremely low energy usage. This work reports the resistive switching (RS) characteristics of the hydrothermally synthesized iron tungstate (FeWO4) based thin film memristive device. The detailed physicochemical analysis was investigated using Rietveld's refinement, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) techniques. The fabricated Ag/FWO/FTO memristive device exhibits bipolar resistive switching (BRS) behavior. In addition, the devices exhibit negative differential resistance (NDR) at both positive and negative bias. The charge-flux relation portrayed the non-ideal or memristive nature of the devices. The reliability in the RS process was analyzed in detail using Weibull distribution and time series analysis techniques. The device exhibits stable and multilevel endurance and retention characteristics which demonstrates the suitability of the device for the high-density non-volatile memory application. The current conduction of the device was dominated by Ohmic and trap controlled-space charge limited current (TC-SCLC) mechanisms and filamentary RS process responsible for the BRS in the device. In a nutshell, the present investigations reveal the potential use of the iron tungstate for the fabrication of memristive devices for the non-volatile memory application.

CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications.

Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics. In particular, the optimized Ag/collagen NFs/fluorine-doped tin oxide RS device shows a voltage-tunable analog memory behavior and good nonvolatile memory properties. Moreover, it can also mimic various biological synaptic learning properties and can be used for pattern classification applications with the help of the spiking neural network. The time series analysis technique is employed to model and predict the switching variations of the RS device. Moreover, the collagen NFs have shown good cytotoxicity and anticancer properties, suggesting excellent biocompatibility as a switching layer. The biocompatibility of collagen NFs is explored with the help of NRK-52E (Normal Rat Kidney cell line) and MCF-7 (Michigan Cancer Foundation-7 cancer cell line). Additionally, the biodegradability of the device is evaluated through a physical transient test. This work provides a vital step toward developing a biocompatible and biodegradable switching material for sustainable nonvolatile memory and neuromorphic computing applications.

Solvothermal synthesis of TiO2nanospheres for non-volatile memory and synaptic learning applications.

In this study, we used the one-pot solvothermal method to synthesize the TiO2nanospheres (NSs) and used them for non-volatile memory and neuromorphic computing applications. Several analytical tools were used to understand the structural, optical, morphological, and compositional characteristics of synthesized TiO2NSs. The tetragonal crystal structure of anatase TiO2was formed, according to the Rietveld refined x-ray diffraction results. The NS morphology was confirmed by field emission scanning electron microscopy and transmission electron microscopy images. X-ray photoelectron spectroscopy was probed to understand the elemental composition and electronic states of the TiO2NSs. We specifically looked at the impact of reaction time on the structural, optical, morphological, compositional, and resistive switching (RS) properties of TiO2NSs. The fabricated devices (Ag/TiO2NSs/FTO) exhibit bipolar RS behavior. The optimized RS device shows good endurance (5000 cycles) and memory retention (5000 s) properties. Moreover, fabricated devices showed double-valued charge-flux characteristics, whereas charge transport was caused by the Ohmic and space charge-limited current mechanisms. Additionally, the optimized device can mimic various synaptic characteristics including potentiation-depression, excitatory post-synaptic current, and paired-pulse facilitation.

Self-Assembled Lanthanum Oxide Nanoflakes by Electrodeposition Technique for Resistive Switching Memory and Artificial Synaptic Devices.

In recent years, many metal oxides have been rigorously studied to be employed as solid electrolytes for resistive switching (RS) devices. Among these solid electrolytes, lanthanum oxide (La2 O3 ) is comparatively less explored for RS applications. Given this, the present work focuses on the electrodeposition of La2 O3 switching layers and the investigation of their RS properties for memory and neuromorphic computing applications. Initially, the electrodeposited La2 O3 switching layers are thoroughly characterized by various analytical techniques. The electrochemical impedance spectroscopy (EIS) and Mott-Schottky techniques are probed to understand the in situ electrodeposition, RS mechanism, and n-type semiconducting nature of the fabricated La2 O3 switching layers. All the fabricated devices exhibit bipolar RS characteristics with excellent endurance and stable retention. Moreover, the device mimics the various bio-synaptic properties such as potentiation-depression, excitatory post-synaptic currents, and paired-pulse facilitation. It is demonstrated that the fabricated devices are non-ideal memristors based on double-valued charge-flux characteristics. The switching variation of the device is studied using the Weibull distribution technique and modeled and predicted by the time series analysis technique. Based on electrical and EIS results, a possible filamentary-based RS mechanism is suggested. The present results assert that La2 O3 is a promising solid electrolyte for memory and brain-inspired applications.

Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications.

Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.

Sprayed FeWO4 thin film-based memristive device with negative differential resistance effect for non-volatile memory and synaptic learning applications.

Resistive switching (RS) memories have attracted great attention as promising solutions to next-generation non-volatile memories and computing technologies because of their simple device configuration, high on/off ratio, low power consumption, fast switching, long retention, and significant cyclic stability. In this work, uniform and adherent iron tungstate (FeWO4) thin films were synthesized by the spray pyrolysis method with various precursor solution volumes, and these were tested as a switching layer for the fabrication of Ag/FWO/FTO memristive devices. The detailed structural investigation was done through various analytical and physio-chemical characterizations viz. X-ray diffraction (XRD) and its Rietveld refinement, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) techniques. The results reveal the pure and single-phase FeWO4 compound thin film formation. Surface morphological study shows the spherical particle formation having a diameter in the range of 20 to 40 nm. The RS characteristics of the Ag/FWO/FTO memristive device demonstrate non-volatile memory characteristics with significant endurance and retention properties. Interestingly, the memory devices show stable and reproducible negative differential resistance (NDR) effects. The in-depth statistical analysis suggests the good operational uniformity of the device. Moreover, the switching voltages of the Ag/FWO/FTO memristive device were modeled using the time series analysis technique by utilizing Holt's Winter Exponential Smoothing (HWES) approach. Additionally, the device mimics bio-synaptic properties such as potentiation/depression, excitatory post-synaptic current (EPSC), and spike-timing-dependent plasticity (STDP) learning rules. For the present device, the space-charge-limited current (SCLC) and trap-controlled-SCLC effects dominated during positive and negative bias I-V characteristics, respectively. The RS mechanism dominated in the low resistance state (LRS), and the high resistance state (HRS) was explained based on the formation and rupture of conductive filament composed of Ag ions and oxygen vacancies. This work demonstrates the RS in the metal tungstate-based memristive devices and demonstrates a low-cost approach for fabricating memristive devices.

Recent progress in energy, environment, and electronic applications of MXene nanomaterials.

Since the discovery of graphene, two-dimensional (2D) materials have gained widespread attention, owing to their appealing properties for various technological applications. Etched from their parent MAX phases, MXene is a newly emerged 2D material that was first reported in 2011. Since then, a lot of theoretical and experimental work has been done on more than 30 MXene structures for various applications. Given this, in the present review, we have tried to cover the multidisciplinary aspects of MXene including its structures, synthesis methods, and electronic, mechanical, optoelectronic, and magnetic properties. From an application point of view, we explore MXene-based supercapacitors, gas sensors, strain sensors, biosensors, electromagnetic interference shielding, microwave absorption, memristors, and artificial synaptic devices. Also, the impact of MXene-based materials on the characteristics of respective applications is systematically explored. This review provides the current status of MXene nanomaterials for various applications and possible future developments in this field.

Unraveling the importance of fabrication parameters of copper oxide-based resistive switching memory devices by machine learning techniques.

In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type features as compared to a linear model and artificial neural network (ANN). Moreover, the DT algorithm predicts the performance of categorical-type features very well. The feature importance score was calculated for each continuous and categorical feature by the gradient boosting (GB) algorithm. Finally, the suggested ML guidelines were employed to fabricate the copper oxide-based RS device and demonstrated its non-volatile memory properties. The results of ML algorithms and experimental devices are in good agreement with each other, suggesting the importance of ML techniques for understanding and optimizing memory devices.

Analysis and Prediction of Hydrothermally Synthesized ZnO-Based Dye-Sensitized Solar Cell Properties Using Statistical and Machine-Learning Techniques.

Dye-sensitized solar cells (DSSCs) are one of the most versatile and low-cost solar cells. However, DSSCs are prone to low power conversion efficiency (PCE) compared to their counterparts, owing to their different synthesis parameters and process conditions. Therefore, designing efficient DSSCs and identifying the parameters that control the PCE of DSSCs are a critical tasks. We have collected data from hydrothermally synthesized DSSCs in the present work, published from 2005 to 2020. In line with publishing trends in the said period, we evaluate ZnO as a popular photoactive material for DSSC applications. We further analyzed the performance of hydrothermally synthesized ZnO DSSCs using different statistical techniques and provided some significant insights. We further applied the machine-learning technique with a decision tree algorithm to understand and discover the possible set of rules and heuristics that govern the morphology of the hydrothermally grown ZnO. In addition, we also employed supervised and unsupervised machine-learning models using conventional decision trees and classification and regression trees, respectively, to identify the dependence of the PCE of ZnO DSSCs on the different synthesis parameters. The reported work also evidences the PCE predictions of the ZnO DSSCs by using random forest and artificial neural network algorithms. The results substantiate that the random forest and artificial neural network algorithms successfully predict the PCE of the ZnO DSSCs with reasonable accuracy. Thus, we present a novel approach of applying statistical analysis and machine-learning algorithms to understand, discover, and predict the performance of DSSCs. We recommend extending the said know-how to other solar cells to identify rules and heuristics and experimentally realize highly efficient solar cells in shrinking manufacturing windows with a cost-effective approach.

Structural and Optical Properties of Nanocrystalline TiO2 with Multiwalled Carbon Nanotubes and Its Photovoltaic Studies Using Ru(II) Sensitizers.

In this study, the in situ sol-gel method has been deployed to prepare the titanium dioxide/multiwalled carbon nanotubes (TiO2/MWCNTs) nanocomposite (NCs) powders with varying content of MWCNTs (0.01-1.0 wt %), to construct the dye-sensitized solar cells (DSSCs). First, binder-free NCs were deposited on a transparent-conducting F:SnO2 (FTO) glass substrate by a doctor-blade technique and then anchored with Ru(II)-based dyes to either N719 or ruthenium phthalocyanine (RuPc). The structural and optical properties and interconnectivity of the materials within the composite are investigated thoroughly by various spectral techniques (XRD, XPS, Raman, FT-IR, and UV-vis), electron microscopy (HRTEM), and BET analysis. The experimental results suggest that the ratio of MWCNTs and TiO2 in NCs, morphology, and their interconnectivity influenced their structural, optical, and photovoltaic properties significantly. Finally, the photovoltaic performances of the assembled DSSCs with different content of MWCNTs to TiO2 films anchored with two different dyes were tested under one sun irradiation (100 mW/cm2). The measured current-voltage (IV) curve and incident photon-to-current conversion efficiency (IPCE) spectra of TiO2/0.1 wt % MWCNTs (T@0.1 C) for N719 dye show three times more power conversion efficiency (η = 6.21%) which is opposed to an efficiency (η = 2.07%) of T@0.1 C for RuPc dye under the same operating conditions.