Conductive bridge random access memory (CBRAM) has been regarded as a promising candidate for the next-generation nonvolatile memory technology. Even with the great performance of CBRAM, the global generation and overinjection of cations after much repetitive switching cannot be prevented. The overinjection of cations into an electrolyte layer causes high-resistancestate resistance (R HRS ) degradation, on/off ratio reduction, and eventual switching failure. It also degrades the switching uniformity. In this work, a Cu-cone-structure-embedded TiN/TiO 2 /Cu cone/TiN device is fabricated to alleviate the problems of Cu-based CBRAM, mentioned above. The fabrication method of the device, which is useful for laboratory scale experiment, is developed, and its superior switching performance and reliability compared with the conventional planar device. The insertion of the Cu cone structure allows the placement of only a limited amount of cation source in each cell, and the embedded conical structure also concentrates the applied electric field, which enables filament growth control. Furthermore, the concentrated field localizes the resistive switching on the tip area of the cone structure, which makes the effective switching area about tens of nanometers even for the much larger area of the entire electrode (several µm 2 ).

The thin-film growth conditions in a plasma-enhanced atomic layer deposition for the (3.0–4.5) nm thick HfO2 film were optimized to use the film as the resistive switching element in a neuromorphic circuit. The film was intended to be used as a feasible synapse with analog-type conductance-tuning capability. The 4.5 nm thick HfO2 films on both conventional TiN and a new RuO2 bottom electrode required the electroforming process for them to operate as a feasible resistive switching memory, which was the primary source of the undesirable characteristics as the synapse. Therefore, electroforming-free performance was necessary, which could be accomplished by thinning the HfO2 film down to 3.0 nm. However, the device with only the RuO2 bottom electrode offered the desired functionality without involving too high leakage or shorting problems, which are due to the recovery of the stoichiometric composition of the HfO2 near the RuO2 layer. In conjunction with the Ta top electrode, which provided the necessary oxygen vacancies to the HfO2 layer, and the high functionality of the RuO2 as the scavenger of excessive incorporated oxygen vacancies, which appeared to be inevitable during the repeated switching operation, the Ta/3.0 nm HfO2/RuO2 provided a highly useful synaptic device component in the neuromorphic hardware system.

The retention behavior of a HfO2 resistive switching memory device with a diameter of 28 nm and an ultra-thin (1 nm) HfO2 layer as the switching layer was examined. Ta and TiN served as the oxygen vacancy (VO) supplying the top and inert bottom electrodes, respectively. Unlike the retention failure phenomenon reported in other thicker oxide-based resistance switching memory devices, the current of both the low and high resistance states suddenly increased at a certain time, causing retention failure. Through the retention tests of the devices in different resistance states, it was concluded that the involvement of the reset step induced the retention failure. The pristine device contained a high portion of VO-rich region and the location of the border between the VO-rich and VO-free regions played the critical role in governing the retention performance. During the reset step, this borderline moves towards the Ta electrode, but moves back to the original location during the retention period, which eventually induces the reconnection of the disconnected conducting filament (in a high resistance state) or strengthens the connected weak portion (low resistance state). The activation energy for the retention failure mechanism was 0.15 eV, which is related to the ionization of neutral VO to ionized VO.

The high nonuniformity and low endurance of the resistive switching random access memory (RRAM) are the two major remaining hurdles at the device level for mass production. Incremental step pulse programming (ISPP) can be a viable solution to the former problem, but the latter problem requires material level innovation. In valence change RRAM, electrodes have usually been regarded as inert (e.g., Pt or TiN) or oxygen vacancy (V) sources (e.g., Ta), but different electrode materials can serve as a sink of V. In this work, an RRAM using a 1.5 nm-thick TaO switching layer is presented, where one of the electrodes was V-supplying Ta and the other was either inert TiN or V-sinking RuO. Whereas TiN could not remove the excessive V in the memory cell, RuO absorbed the unnecessary V. By carefully tuning (balancing) the capabilities of V-supplying Ta and V-sinking RuO electrodes, an almost invariant ISPP voltage and a greatly enhanced endurance performance can be achieved.

Chalcogenide materials have been regarded as strong candidates for both resistor and selector elements in passive crossbar arrays owing to their dual capabilities of undergoing threshold and resistance switching. This work describes the bipolar resistive switching (BRS) of amorphous GeSe thin films, which used to show Ovonic threshold switching (OTS) behavior. The behavior of this new functionality of the material follows filament-based resistance switching when Ti and TiN are adopted as the top and bottom electrodes, respectively. The detailed analysis revealed that the high chemical affinity of Ti to Se produces a Se-deficient Ge x Se1–x matrix and the interfacial Ti–Se layer. Electroforming-free BRS behavior with reliable retention and cycling endurance was achieved. The performance improvement was attributed to the Ti–Se interfacial layer, which stabilizes the composition of GeSe during the electrical switching cycles by preventing further massive Se migration to the top electrode. The conduction mechanism analysis denotes that the resistance switching originates from the formation and rupture of the high-conductance semiconducting Ge-rich Ge x Se1–x filament. The high-resistance state follows the modified Poole–Frenkel conduction.

Valence change-type resistance switching behaviors in oxides can be understood by well-established physical models describing the field-driven oxygen vacancy distribution change. In those models, electroformed residual oxygen vacancy filaments are crucial as they work as an electric field concentrator and limit the oxygen vacancy movement along the vertical direction. Therefore, their movement outward by diffusion is negligible. However, this situation may not be applicable in the electroforming-free system, where the field-driven movement is less prominent, and the isotropic oxygen vacancy diffusion by concentration gradient is more significant, which has not been given much consideration in the conventional model. Here, we propose a modified physical model that considers the change in the oxygen vacancies’ charged state depending on their concentrations and the resulting change in diffusivity during switching to interpret the electroforming-free device behaviors. The model suggests formation of an hourglass-shaped filament constituting a lower concentration of oxygen vacancies due to the fluid oxygen diffusion in the thin oxide. Consequently, the proposed model can explain the electroforming-free device behaviors, including the retention failure mechanism, and suggest an optimized filament configuration for improved retention characteristics. The proposed model can plausibly explain both the electroformed and the electroforming-free devices. Therefore, it can be a standard model for valence change memristors.

Herein, a feasible method is provided for circuit implementation of the convolutional neural network (CNN) in neuromorphic hardware using the multiple layers‐stacked resistance switching random access memory (ReRAM). The specific ReRAM is accompanied by self‐rectification functionality. The single‐input multiple‐output (SIMO) scheme is an optimum method in the extraction of the features of a letter with a versatile selection of the intended features, whereas the multiple‐input single‐output (MISO) scheme provides a highly efficient method to extract the features from the color image, which is composed of several component color images. The Pt/HfO2−x/TiN‐based self‐rectification ReRAM that is integrated into the sidewalls of the two‐layer structure provides a sound framework for the circuit implementation of the SIMO and MISO schemes. The appropriate selection of the kernels for image compression and feature extraction greatly facilitates the CNN in neuromorphic hardware.

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