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Abstract: Recently memristors have emerged as a form of nonvolatile memory that is based on the principle of ion transport in solid electrolytes under the impact of an external electric field. It is perceived as one of the key elements to building next-generation computing systems owing to its peculiar resistive switching characteristics. The switching mechanism in a memristor is mainly governed by filamentary conduction. Further, it can be employed as a memory as well as a logic element, which makes it an ideal candidate for building innovative computer architecture. Moreover, it is capable of mimicking the characteristics of biological synapses, which makes it an ideal candidate for developing a Neuromorphic system. In this review to begin with the switching mechanism of the memristor, primarily focusing on filamentary conduction, is discussed. Few SPICE models of memristor are reviewed, and their critical comparison is performed, which are widely used to build computing systems. An in-depth study on the various crossbar memory architecture augmented with memristors is reviewed. Finally, the application of memristors in neuromorphic computing and hardware implementation of Artificial Neural Networks (ANN) employing memristors is discussed.

Introduction: We report here our success in developing a flexible RRAM stack structure by employing a low-cost method. Bare conductive commercial electric paint is used as anode against Stainless Steel (SS) foil deposited with Mn3O4 thin films forming a BCEP/Mn3O4/SS thin film stack to understand the intrinsic non-volatile resistive switching behavior of Mn3O4. Experimental: Thin film Mn3O4 is deposited on a SS (304) foil by means of potential sweep voltammetry by maintaining typical conditions. Interestingly, the pristine device is subjected to an electroforming process which exhibited a digital type bipolar resistive switching characteristics. The study of the conduction mechanism revealed that the resistive switching arises due to local effect occurring in the bulk of Mn3O4, which corresponds to the growth and annihilation of oxygen vacancy nanofilaments, and this is responsible for the change in resistance state of the RRAM between Low Resistance State (LRS) and High Resistance State (HRS) respectively. Results: In order to affirm the reliability and reproducibility of RRAM structure, the memory retention is monitored over 103 s and subsequently, the endurance test is also carried out ensuring the reproducibility over 100 cycles. Conclusion: Owing to the flexible nature of BCEP/Mn3O4/SS Foil RRAM stack structure, it is perceived to be a prime candidate for future non-volatile memory and flexible electronics applications.

Ion transport in the solid state has been regarded as imperative with regards to high energy density electrochemical storage devices (for instance, batteries) for efficient electric mobility. Of late, there is another niche application involving ion transport in solid state which manifested itself as nonvolatile memory namely memristor. Such memories are classified under the emerging category of novel solid state Resistive Random Access Memories (RRAM). In 2008, HP labs unveiled the first practical memristor device employing TiO2 and non-stoichiometric titania as bilayer stack structure and on both sides of two titania layers platinum (pt) are used as blocking electrode for ions. It is understood that switching fundamentals are correlated to the filamentary conduction in metal oxide memristors owing to the formation and rupture of the filament-like nano-dendrites, one of the key mechanisms widely accepted in the arena of memristor analysis. This paper critically reviews the fundamental materials being employed in novel memristor memories. It is believed that solid electrolytes (fast ion conductors) are the fundamental building blocks of these memories. We have chosen a few archetypes, solid electrolytes are considered and their impact on the state-of-art research in this domain is discussed in detail. An indepth analysis of the fundamentals of resistive switching mechanism involved in various classes of memristive devices viz., Electrochemical Metallization Memories (ECM) and Valence Change Memories (VCM) is elucidated. A few important applications of memristors such as neuristor and artificial synapse in neuromorphic computing are reviewed as well.