Muxi Yu

Novel Vertical 3D Structure of TaOx-based RRAM with Self-localized Switching Region by Sidewall Electrode Oxidation

A novel vertical 3D RRAM structure with greatly improved reliability behavior is proposed and experimentally demonstrated through basically compatible process featuring self-localized switching region by sidewall electrode oxidation. Compared with the conventional structure, due to the effective confinement of the switching region, the newly-proposed structure shows about two orders higher endurance (>108 without verification operation) and better retention (>180h@150 °C), as well as high uniformity. Corresponding model is put forward, on the base of which thorough theoretical analysis and calculations are conducted as well, demonstrating that, resulting from the physically-isolated switching from neighboring cells, the proposed structure exhibits dramatically improved reliability due to effective suppression of thermal effects and oxygen vacancies diffusion interference, indicating that this novel structure is very promising for future high density 3D RRAM application.

Encapsulation layer design and scalability in encapsulated vertical 3D RRAM

Here we propose a novel encapsulated vertical 3D RRAM structure with each resistive switching cell encapsulated by dielectric layers, contributing to both the reliability improvement of individual cells and thermal disturbance reduction of adjacent cells due to the effective suppression of unwanted oxygen vacancy diffusion. In contrast to the traditional vertical 3D RRAM, encapsulated bar-electrodes are adopted in the proposed structure substituting the previous plane-electrodes, thus encapsulated resistive switching cells can be naturally formed by simply oxidizing the tip of the metal bar-electrodes. In this work, TaO x -based 3D RRAM devices with SiO2 and Si3N4 as encapsulation layers are demonstrated, both showing significant advantages over traditional unencapsulated vertical 3D RRAM. Furthermore, it was found thermal conductivity and oxygen blocking ability are two key parameters of the encapsulation layer design influencing the scalability of vertical 3D RRAM. Experimental and simulation data show that oxygen blocking ability is more critical for encapsulation layers in the relatively large scale, while thermal conductivity becomes dominant as the stacking layers scale to the sub-10 nm regime. Finally, based on the notable impacts of the encapsulation layer on 3D RRAM scaling, an encapsulation material with both excellent oxygen blocking ability and high thermal conductivity such as AlN is suggested to be highly desirable to maximize the advantages of the proposed encapsulated structure. The findings in this work could pave the way for reliable ultrahigh-density storage applications in the big data era.

Self-selection effects and modulation of TaOx resistive switching random access memory with bottom electrode of highly doped Si

In this paper, we propose a TaOx resistive switching random access memory (RRAM) device with operation-polarity-dependent self-selection effect by introducing highly doped silicon (Si) electrode, which is promising for large-scale integration. It is observed that with highly doped Si as the bottom electrode (BE), the RRAM devices show non-linear (>103) I-V characteristic during negative Forming/Set operation and linear behavior during positive Forming/Set operation. The underling mechanisms for the linear and non-linear behaviors at low resistance states of the proposed device are extensively investigated by varying operation modes, different metal electrodes, and Si doping type. Experimental data and theoretical analysis demonstrate that the operation-polarity-dependent self-selection effect in our devices originates from the Schottky barrier between the TaOx layer and the interfacial SiOx formed by reaction between highly doped Si BE and immigrated oxygen ions in the conductive filament area.

Microscopic origin of read current noise in TaOx-based resistive switching memory by ultra-low temperature measurement

TaOx-based resistive random access memory (RRAM) attracts considerable attention for the development of next generation nonvolatile memories. However, read current noise in RRAM is one of the critical concerns for storage application, and its microscopic origin is still under debate. In this work, the read current noise in TaOx-based RRAM was studied thoroughly. Based on a noise power spectral density analysis at room temperature and at ultra-low temperature of 25 K, discrete random telegraph noise (RTN) and continuous average current fluctuation (ACF) are identified and decoupled from the total read current noise in TaOx RRAM devices. A statistical comparison of noise amplitude further reveals that ACF depends strongly on the temperature, whereas RTN is independent of the temperature. Measurement results combined with conduction mechanism analysis show that RTN in TaOx RRAM devices arises from electron trapping/detrapping process in the hopping conduction, and ACF is originated from the thermal activatio...

A tantalum oxide memristor for artificial synapse applications

Memristor has attracted significant attention in recent years because of its capability to act as artificial synapse in neuromorphic systems. In this paper, a memristor is demonstrated based on tantalum oxide. Multiple resistance states can be achieved. The resistance switching mechanism of continuous sets and resets is discussed. The essential synaptic behaviors of potentiation and depression are achieved.

An electronic synapse based on tantalum oxide material

An electronic synaptic device with weight modulation and learning function is experimentally demonstrated based on a memristor with Ti/Ta2O5/TaOx/Pt structure, which shows good gradual resistance tuning characteristics. The device exhibits various synaptic functions including potentiation, depression, short/long term plasticity (STP/LTP) and Spike-Time-Dependent-Plasticity (STDP) under both DC sweep and pulse operations. The effects of modification pulse conditions such as pulse amplitude, width and interval on synaptic weight tuning was experimentally investigated. Moreover, the learning behavior similar to memory and consolidation in human brains is obtained in our device, indicating it is a promising candidate device for neural network implementation.