A magnetic field controlled memristor towards the design of an implantable detector.

Memristors, which combine the behaviors of memory and resistive switching (RS), have a wide application prospect in information processing and artificial neural networks. The RS memory behaviors of memristors are primarily determined by the functional layer materials, device structure, and working conditions. Herein, a CuMnO2 nanomaterial with the manganese copper ore structure was prepared on a Ti substrate by hydrothermal method, and a memristor with the Ag/CuMnO2/Ti sandwich structure was developed. The RS memory behavior of the as-prepared memristor can be regulated through a low magnetic field (MF), and thus the resistance value of device shows a multi-level resistance states. Compared with other regulation factors, the MF can remotely adjust and control the RS characteristics of memristor, which is a non-invasive and non-destructive regulatory means. The MF regulated memristor can not only be used for multi-level high-density information storage, but also it can protect the health of special populations by identifying the MF intensity of the surrounding environment. When the device is operated in an MF environment, the change of resistance value of the device in both high resistance state (HRS) and low resistance state (LRS) is mainly attributed to the influence of Loren magnetic force on conductive ions.

Memristor-based neural networks: a bridge from device to artificial intelligence.

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

Applications of biomemristors in next generation wearable electronics.

With the rapid development of mobile internet and artificial intelligence, wearable electronic devices have a great market prospect. In particular, information storage and processing of real-time collected data are an indispensable part of wearable electronic devices. Biomaterial-based memristive systems are suitable for storage and processing of the obtained information in wearable electronics due to the accompanying merits, i.e. sustainability, lightweight, degradability, low power consumption, flexibility and biocompatibility. So far, many biomaterial-based flexible and wearable memristive devices were prepared by spin coating or other technologies on a flexible substrate at room temperature. However, mechanical deformation caused by mechanical mismatch between devices and soft tissues leads to the instability of device performance. From the current research and practical application, the device will face great challenges when adapting to different working environments. In fact, some interesting studies have been performed to address the above issues while they were not intensively highlighted and overviewed. Herein, the progress in wearable biomemristive devices is reviewed, and the outlook and perspectives are provided in consideration of the existing challenges during the development of wearable biomemristive systems.

Non-zero-crossing current-voltage hysteresis behavior induced by capacitive effects in bio-memristor.

Capacitive devices have drawn a beautiful application scene in electronic device systems ranging from touch sensors, energy storages and multifunction transistors, but serving as memristive term is still blank. Sweet potato peel (SPP) as function layer was employed to develop the memristive device with Ag/SPP/F-doped SnO2 (FTO) structure. A current-voltage (I-V) hysteresis, which is characterized by a typical capacitive behavior, is impressively observed in the developed device. Nonvolatile data storage is feasible using the non-zero-crossing I-V hysteresis because the resistance states can be well maintained. Charge transfer at the Ag/SPP and SPP/FTO interfaces, and the interplay between Ag+ ions and charges are responsible for this non-zero-crossing I-V hysteresis behaviors. This work possibly gives an insight into the data storage in terms of a new conception electronic device based on environment-friendly material.

A flexible nonvolatile resistive switching memory device based on ZnO film fabricated on a foldable PET substrate.

In this work, a flexible resistive switching memory device based on ZnO film was fabricated using a foldable Polyethylene terephthalate (PET) film as substrate while Ag and Ti acts top and bottom electrode. Our as-prepared device represents an outstanding nonvolatile memory behavior with good "write-read-erase-read" stability at room temperature. Finally, a physical model of Ag conductive filament is constructed to understanding the observed memory characteristics. The work provides a new way for the preparation of flexible memory devices based on ZnO films, and especially provides an experimental basis for the exploration of high-performance and portable nonvolatile resistance random memory (RRAM).

Metal Ions Redox Induced Repeatable Nonvolatile Resistive Switching Memory Behavior in Biomaterials.

The resistance random access memory (RRAM) based on biomaterials has great potential application in the sustainable electronic devices with the advantages of being sustainable, green, and environment-friendly, and it can offer a potential route for developing bio-RRAM devices, which would be a competitive bench in development of multipurpose memory devices. In our work, the banana peel, an ubiquitous useless waste, is introduced as an intermediate insulating material to preparing resistive switching memory device with Ag/Banana peel/Ti structure, in which the superior switching memory performance with a lager high resistance state/low resistance state resistance ratio and long retention characteristics are revealed. Moreover, the coexistence of memristor effect, capacitance effect, and negative differential resistance phenomenon are observed in our device. The repeatable nonvolatile resistive switching memory behaviors are attributed to the redox properties of metal cations contained in biomaterials.

From dead leaves to sustainable organic resistive switching memory.

An environmental-friendly, sustainable, pollution-free, biodegradable, flexible and wearable electronic device hold advanced potential applications. Here, an organic resistive switching memory device with Ag/Leaves/Ti/PET structure on a flexible polyethylene terephthalate (PET) substrate was fabricated for the first time. We observed an obvious resistive switching memory characteristic with large switching resistance ratio and stable cycle performance at room temperature. This work demonstrates that leaves, a useless waste, can be properly treated to make useful devices. Furthermore, the as-fabricated devices can be degraded naturally without damage to the environment.