In the advancement of the semiconductor device technology, ZnO could be a prospective alternative than the other metal oxides for its versatility and huge applications in different aspects. In this review, a thorough overview on ZnO for the application of resistive switching memory (RRAM) devices has been conducted. Various efforts that have been made to investigate and modulate the switching characteristics of ZnO-based switching memory devices are discussed. The use of ZnO layer in different structure, the different types of filament formation, and the different types of switching including complementary switching are reported. By considering the huge interest of transparent devices, this review gives the concrete overview of the present status and prospects of transparent RRAM devices based on ZnO. ZnO-based RRAM can be used for flexible memory devices, which is also covered here. Another challenge in ZnO-based RRAM is that the realization of ultra-thin and low power devices. Nevertheless, ZnO not only offers decent memory properties but also has a unique potential to be used as multifunctional nonvolatile memory devices. The impact of electrode materials, metal doping, stack structures, transparency, and flexibility on resistive switching properties and switching parameters of ZnO-based resistive switching memory devices are briefly compared. This review also covers the different nanostructured-based emerging resistive switching memory devices for low power scalable devices. It may give a valuable insight on developing ZnO-based RRAM and also should encourage researchers to overcome the challenges.

Artificial synapse having good linearity is crucial to achieve an efficient learning process in neuromorphic computing. It is found that the synaptic linearity can be enhanced by engineering the doping region across the switching layer. The nonlinearity of potentiation and depression of the pure device is 36% and 91%, respectively; meanwhile, the nonlinearity after doping can be suppressed to be 22% (potentiation) and 60% (depression). Henceforth, the learning accuracy of the doped device is 91% with only 13 iterations; meanwhile, the pure device is 78%. A detailed conduction mechanism to understand this phenomenon is proposed.

In this work, we provide a comprehensive discussion on the various models proposed for the design and description of resistive random access memory (RRAM), being a nascent technology is heavily reliant on accurate models to develop efficient working designs and standardize its implementation across devices. This review provides detailed information regarding the various physical methodologies considered for developing models for RRAM devices. It covers all the important models reported till now and elucidates their features and limitations. Various additional effects and anomalies arising from memristive system have been addressed, and the solutions provided by the models to these problems have been shown as well. All the fundamental concepts of RRAM model development such as device operation, switching dynamics, and current-voltage relationships are covered in detail in this work. Popular models proposed by Chua, HP Labs, Yakopcic, TEAM, Stanford/ASU, Ielmini, Berco-Tseng, and many others have been compared and analyzed extensively on various parameters. The working and implementations of the window functions like Joglekar, Biolek, Prodromakis, etc. has been presented and compared as well. New well-defined modeling concepts have been discussed which increase the applicability and accuracy of the models. The use of these concepts brings forth several improvements in the existing models, which have been enumerated in this work. Following the template presented, highly accurate models would be developed which will vastly help future model developers and the modeling community.

Numerous metal-insulator-metal systems demonstrate electrically induced resistive switching effects and have therefore been proposed as the basis for future nonvolatile memories. They combine the advantages of flash memories and dynamic random access memories while avoiding their drawbacks, such as operation speed, power consumption and device integration and scalable issues. The RRAM devices primarily operate at different resistance values to store the digital data and can keep the resistance state without any power supply. Recent advances in the understanding of the resistive switching mechanism are explained by a thermal or electrochemical redox reaction near the interface between the oxide and the top active metal electrode. Here we review the ongoing research and development activities on the perovskite based resistive switching memory devices. The possible switching mechanisms for the resistive switching are described. The effects of crystal structure, dopants, doping concentrations, annealing temperature, device structures and thickness of the active oxide layer on the resistive switching characteristics and consequently the memory performances are also discussed. From this insight, we take a brief look into different effect on the switching of the perovskite material systems.