This review article attempts to provide a comprehensive review of the recent progress in the so-called resistive random access memories (RRAMs) First, a brief introduction is presented to describe the construction and development of RRAMs, their potential for broad applications in the fields of nonvolatile memory, unconventional computing and logic devices, and the focus of research concerning RRAMs over the past decade Second, both inorganic and organic materials used in RRAMs are summarized, and their respective advantages and shortcomings are discussed Third, the important switching mechanisms are discussed in depth and are classified into ion migration, charge trapping/de-trapping, thermochemical reaction, exclusive mechanisms in inorganics, and exclusive mechanisms in organics Fourth, attention is given to the application of RRAMs for data storage, including their current performance, methods for performance enhancement, sneak-path issue and possible solutions, and demonstrations of 2-D and 3-D crossbar arrays Fifth, prospective applications of RRAMs in unconventional computing, as well as logic devices and multi-functionalization of RRAMs, are comprehensively summarized and thoroughly discussed The present review article ends with a short discussion concerning the challenges and future prospects of the RRAMs

Recent progress in resistive random access memories: Materials, switching mechanisms, and performance

We study the Mott insulator compound GaTa4Se8 in which we previously discovered an electric-field-induced resistive transition. We show that the resistive switching is associated to the appearance of metallic and super-insulating nanodomains by means of scanning tunneling microscopy/spectroscopy (STM/STS). Moreover, we show that local electronic transitions can be controlled at the nanoscale at room temperature using the electric field of the STM tip. This opens the way for possible applications in resistive random access memories (RRAM) devices.

Resistive switching at the nanoscale in the Mott insulator compound GaTa4Se8.

The approaching end of Moore's law scaling has significantly accelerated multiple fields of research including neuromorphic-, quantum-, and photonic computing, each of which possesses unique benefits unobtained through conventional binary computers. One of the most compelling arguments for neuromorphic computing systems is power consumption, noting that computations made in the human brain are approximately 106times more efficient than conventional CMOS logic. This review article focuses on the materials science and physical mechanisms found in metal chalcogenides that are currently being explored for use in neuromorphic applications. We begin by reviewing the key biological signal generation and transduction mechanisms within neuronal components of mammalian brains and subsequently compare with observed experimental measurements in chalcogenides. With robustness and energy efficiency in mind, we will focus on short-range mechanisms such as structural phase changes and correlated electron systems that can be driven by low-energy stimuli, such as temperature or electric field. We aim to highlight fundamental materials research and existing gaps that need to be overcome to enable further integration or advancement of metal chalcogenides for neuromorphic systems.

Metal chalcogenides for neuromorphic computing: emerging materials and mechanisms.

Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.

Featureless adaptive optimization accelerates functional electronic materials design

Electronic materials exhibiting phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features impeding model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and band gap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.

Featureless adaptive optimization accelerates functional electronic materials design.

The Mott insulator compounds AM4Q8 exhibit a new type of volatile and non volatile resistive switchings that are of interest for RRAM application. We found that above a threshold electric field E
 TH
 of the order of a few kV/cm these compounds undergo a volatile resistive switching based on an avalanche process. For electric field much higher than the threshold avalanche breakdown field, the resistive switching turns non volatile. Our EDXS and STEM analyses show that the non volatile resistive switching originating from the avalanche breakdown can neither be ascribed to local chemical modifications nor to a local phase change with symmetry breaking at a resolution better than a few nanometer. This is in strong contrast with non volatile resistive switching reported so far that are all based on chemical or structural changes. Conversely, our results suggest that the avalanche breakdown induce the collapse of the Mott insulating state at the local scale and the formation of a granular conductive filament formed by compressed metallic domains and expanded “superinsulating” domains.

Electric field induced avalanche breakdown and non-volatile resistive switching in the Mott Insulators AM4Q8

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Electric field induced avalanche breakdown and non-volatile resistive switching in the Mott Insulators AM4Q8