Retention Model of TaO/HfO x and TaO/AlO x RRAM with Self-Rectifying Switch Characteristics

Abstract

A retention behavior model for self-rectifying TaO/HfOx- and TaO/AlOx-based resistive random-access memory (RRAM) is proposed. Trapping-type RRAM can have a high resistance state (HRS) and a low resistance state (LRS); the degradation in a LRS is usually more severe than that in a HRS, because the LRS during the SET process is limited by the internal resistor layer. However, if TaO/AlOx elements are stacked in layers, the LRS retention can be improved. The LRS retention time estimated by extrapolation method is more than 5 years at room temperature. Both TaO/HfOx- and TaO/AlOx-based RRAM structures have the same capping layer of TaO, and the activation energy levels of both types of structures are 0.38 eV. Moreover, the additional AlOx switching layer of a TaO/AlOx structure creates a higher O diffusion barrier that can substantially enhance retention, and the TaO/AlOx structure also shows a quite stable LRS under biased conditions.

Resistive switching of the HfOx/HfO2 bilayer heterostructure and its transmission characteristics as a synapse

“…Nanoscale transition metal oxide-based resistive switching random access memory (RRAM) devices, due to their various virtues, including high operating speed, low power consumption and excellent scalability, have attracted a great deal of attention. 1–4 In recent years, a lot of effort has been focused on RRAM based on a bilayer heterostructure consisting of two different oxides or the same oxide with different oxygen contents in virtue of its peculiar performance, such as multilevel resistive switching, 5–7 self-rectification functionalities, 8–11 and synaptic emulation. 12 RRAM has been proposed to emulate the synapse owing to their similarity with nonlinear transmission characteristics.…”

Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

“…Nevertheless, the extension of the results obtained for the SLP test structures to more practical implementations such as MLPs considering the aforementioned line parasitics is still to be addressed. It is worth pointing out that other memristor device nonidealities threaten the performance of MCA DNNs and are currently the focus of intense research: nonlinearity in the I-V characteristics [26], retention failures [27][28][29], nonuniformity [17,30], Device-to-Device (D2D) and Cycle-to-Cycle (C2C) variability are some of the most representative challenges. However, nonlinearity factors well below 10 have been obtained by optimizing the device fabrication process [31,32] and have also been addressed through specific training [33,34] and voltage mapping [26] methodologies.…”

An Organic Flexible Artificial Bio-Synapses with Long-Term Plasticity for Neuromorphic Computing

“…Flexible electronics are more portable and deformable in comparison with silicon-based devices [ 10 , 11 , 12 ]. However, most RRAM-based synaptic memories are composed of various inorganic materials, such as HfO x , Al 2 O 3 , and ZnO [ 13 , 14 , 15 ]. These inorganic materials usually require high temperature treatment steps with poor stretchability.…”

“…These inorganic materials usually require high temperature treatment steps with poor stretchability. In addition, the intrinsic properties of these materials are not compatible with a flexible substrate, which cannot meet the development and applications of flexible electronics [ 15 , 16 , 17 ]. Therefore, it is urgent to find a type of material suitable for flexible devices.…”