In this review, we provide an overview of our work in resistive switching mechanisms on oxide-based resistance random access memory (RRAM) devices. Based on the investigation of physical and chemical mechanisms, we focus on its materials, device structures, and treatment methods so as to provide an in-depth perspective of state-of-the-art oxide-based RRAM. The critical voltage and constant reaction energy properties were found, which can be used to prospectively modulate voltage and operation time to control RRAM device working performance and forecast material composition. The quantized switching phenomena in RRAM devices were demonstrated at ultra-cryogenic temperature (4K), which is attributed to the atomic-level reaction in metallic filament. In the aspect of chemical mechanisms, we use the Coulomb Faraday theorem to investigate the chemical reaction equations of RRAM for the first time. We can clearly observe that the first-order reaction series is the basis for chemical reaction during reset process in the study. Furthermore, the activation energy of chemical reactions can be extracted by changing temperature during the reset process, from which the oxygen ion reaction process can be found in the RRAM device. As for its materials, silicon oxide is compatible to semiconductor fabrication lines. It is especially promising for the silicon oxide-doped metal technology to be introduced into the industry. Based on that, double-ended graphene oxide-doped silicon oxide based via-structure RRAM with filament self-aligning formation, and self-current limiting operation ability is demonstrated. The outstanding device characteristics are attributed to the oxidation and reduction of graphene oxide flakes formed during the sputter process. Besides, we have also adopted a new concept of supercritical CO2 fluid treatment to efficiently reduce the operation current of RRAM devices for portable electronic applications.

Homeothermic synaptic behaviors with a wide range of temperature were demonstrated in CMOS-compatible HfO _x /AlO _y memristors.

The ever‐increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low‐power, high‐speed, and noise‐tolerant computing capabilities of the brain, may provide such a shift. Many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop neuromorphic computing platforms. These platforms are being designed using various hardware technologies, including the well‐established complementary metal‐oxide semiconductor (CMOS), and emerging memristive technologies such as SiO_x‐based memristors. Herein, recent progress in CMOS, SiO_x‐based memristive, and mixed CMOS‐memristive hardware for neuromorphic systems is highlighted. New and published results from various devices are provided that are developed to replicate selected functions of neurons, synapses, and simple spiking networks. It is shown that the CMOS and memristive devices are assembled in different neuromorphic learning platforms to perform simple cognitive tasks such as classification of spike rate‐based patterns or handwritten digits. Herein, it is envisioned that what is demonstrated is useful to the unconventional computing research community by providing insights into advances in neuromorphic hardware technologies.