Effect of growth temperature on self-rectifying BaTiO3/ZnO heterojunction for high-density crossbar arrays and neuromorphic computing.

In the quest for high-density integration and massive scalability, ferroelectric-based devices provide an achievable approach for nonvolatile crossbar array (CBA) architecture and neuromorphic computing. In this report, ferroelectric-semiconductor (Pt/BaTiO3/ZnO/Au) heterojunction-based devices are demonstrated to exhibit nonvolatile and synaptic characteristics. In this study, the ferroelectric (BaTiO3) layer was modulated at various growth temperatures of 350 °C, 450 °C, 550 °C and 650 °C. Growing temperature in the ferroelectric layer has a significant impact on resistive switching. The ferroelectricity of the BaTiO3 thin film enhanced by increasing temperature causes a substantial shift in the interface state density at heterojunction interface, which is crucial for self-rectification. Furthermore, this self-rectifying property advances to reduce the crosstalk problem without any selector device. Enhanced resistive switching and neuromorphic applications have been demonstrated using BaTiO3 heterostructure devices at 550 °C. The dynamic ferroelectric polarization switching in this heterojunction demonstrated linear conductance change in artificial synapses with 91 % recognition accuracy. Ferroelectric polarization reversal with a depletion region at the heterojunction interface is the responsible mechanism for the switching in these devices. Thus, these findings pave the way for designing low power high-density crossbar arrays and neuromorphic application based on ferroelectric-semiconductor heterostructures.

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.

Optically Reconfigurable Complementary Logic Gates Enabled by Bipolar Photoresponse in Gallium Selenide Memtransistor.

To avoid the complexity of the circuit for in-memory computing, simultaneous execution of multiple logic gates (OR, AND, NOR, and NAND) and memory behavior are demonstrated in a single device of oxygen plasma-treated gallium selenide (GaSe) memtransistor. Resistive switching behavior with RON /ROFF ratio in the range of 104 to 106 is obtained depending on the channel length (150 to 1600 nm). Oxygen plasma treatment on GaSe film created shallow and deep-level defect states, which exhibit carriers trapping/de-trapping, that lead to negative and positive photoconductance at positive and negative gate voltages, respectively. This distinguishing feature of gate-dependent transition of negative to positive photoconductance encourages the execution of four logic gates in the single memory device, which is elusive in conventional memtransistor. Additionally, it is feasible to reversibly switch between two logic gates by just adjusting the gate voltages, e.g., NAND/NOR and AND/NAND. All logic gates presented high stability. Additionally, memtransistor array (1×8) is fabricated and programmed into binary bits representing ASCII (American Standard Code for Information Interchange) code for the uppercase letter "N". This facile device configuration can provide the functionality of both logic and memory devices for emerging neuromorphic computing.

Flexible Organic-Inorganic Halide Perovskite-Based Diffusive Memristor for Artificial Nociceptors.

With the current evolution in the artificial intelligence technology, more biomimetic functions are essential to execute increasingly complicated tasks and respond to challenging work environments. Therefore, an artificial nociceptor plays a significant role in the advancement of humanoid robots. Organic-inorganic halide perovskites (OHPs) have the potential to mimic the biological neurons due to their inherent ion migration. Herein, a versatile and reliable diffusive memristor built on an OHP is reported as an artificial nociceptor. This OHP diffusive memristor showed threshold switching properties with excellent uniformity, forming-free behavior, a high ION/IOFF ratio (104), and bending endurance over >102 cycles. To emulate the biological nociceptor functionalities, four significant characteristics of the artificial nociceptor, such as threshold, no adaptation, relaxation, and sensitization, are demonstrated. Further, the feasibility of OHP nociceptors in artificial intelligence is being investigated by fabricating a thermoreceptor system. These findings suggest a prospective application of an OHP-based diffusive memristor in the future neuromorphic intelligence platform.

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1.

With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Stable and Multilevel Data Storage Resistive Switching of Organic Bulk Heterojunction.

Organic nonvolatile memory devices have a vital role for the next generation of electrical memory units, due to their large scalability and low-cost fabrication techniques. Here, we show bipolar resistive switching based on an Ag/ZnO/P3HT-PCBM/ITO device in which P3HT-PCBM acts as an organic heterojunction with inorganic ZnO protective layer. The prepared memory device has consistent DC endurance (500 cycles), retention properties (104 s), high ON/OFF ratio (105), and environmental stability. The observation of bipolar resistive switching is attributed to creation and rupture of the Ag filament. In addition, our conductive bridge random access memory (CBRAM) device has adequate regulation of the current compliance leads to multilevel resistive switching of a high data density storage.