3D Vertical Resistive Random Access Memory Research Articles

Demonstration of Synaptic Characteristics in VRRAM with TiN Nanocrystals for Neuromorphic System

AbstractTo efficiently develop an extremely intensive storage memory, the resistive random‐access memory (RRAM), which operates by producing and rupturing conductive filaments, is essential. However, due to the stochastic nature of filament production, this filamentary type resistive switching has an inherent limitation, which entails the unpredictability of the driving voltage and resistance states. Several strategies such as doping, research into multilayer stacks, and interface engineering, are suggested to tackle this challenge. This work fabricates a CMOS‐compatible TiN/HfOx/TiN‐NCs (nanocrystals)/HfOx/TiN RRAM to implement analog resistive switching and advance the development of the synaptic device. Specifically, atomic force microscopy (AFM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) are utilized to observe the formation of TiN nanocrystals, which play a crucial role in the enhancement of resistive switching. By comparing HfOx–based RRAM devices with and without NCs, the DC I–V curves, retention, endurance, and switching speed are properly examined. Interestingly, it is found that the TiN/HfOx/TiN‐NCs/HfOx/TiN device is more appropriately utilized as an artificial synapse in neuromorphic systems mainly due to its stable and reliable resistive switching properties. Finally, this work demonstrates well‐controlled resistive switching 3D vertical RRAM with TiN‐NCs, which is particularly suitable for high‐density memory.

Homogeneous 3D Vertical Integration of Parylene‐C Based Organic Flexible Resistive Memory on Standard CMOS Platform

Abstract3D integration of vertical resistive random access memory (VRRAM) with organic materials is promising for ultra‐high density flexible data storage. However, it is extremely challenging to heterogeneously fabricate an organic 3D VRRAM due to complicated issues such as chemical/thermal robustness, compatibility of organic/inorganic materials, and processes of stacking/patterning multi‐layer organic/metal films. Herein, an organic flexible 3D VRRAM based on parylene‐C is experimentally demonstrated on a standard complementary metal oxide semiconductor platform for the first time. The proposed 3D VRRAM can be homogenously fabricated based solely on parylene‐C (except the metal electrodes), significantly facilitating the 3D integration owing to chemical stability and compatibility with standard photolithography patterning. The flexible 3D organic memory arrays demonstrate great memory characteristics including retention time >105 s, endurance cycles >300, and resistance ON/OFF ratio >10. Further, a finite element analysis is established to model and investigate the scaling potential of the organic 3D VRRAM. The simulation results indicate the elevated temperature during programming could be catastrophic for ultra‐high density 3D VRRAM if the feature size approaches ≈100 nm or below, urging the containment of programming power to avoid thermal issues as well as to save energy consumption.

Effect of Initial Synaptic State on Pattern Classification Accuracy of 3D Vertical Resistive Random Access Memory (VRRAM) Synapses

Amidst the considerable attention artificial intelligence (AI) has attracted in recent years, a neuromorphic chip that mimics the biological neuron has emerged as a promising technology. Memristor or Resistive random-access memory (RRAM) is widely used to implement a synaptic device. Recently, 3D vertical RRAM (VRRAM) has become a promising candidate to reducing resistive memory bit cost. This study investigates the operation principle of synapse in 3D VRRAM architecture. In these devices, the classification response current through a vertical pillar is set by applying a training algorithm to the memristors. The accuracy of neural networks with 3D VRRAM synapses was verified by using the HSPICE simulator to classify the alphabet in 7×7 character images. This simulation demonstrated that 3D VRRAMs are usable as synapses in a neural network system and that a 3D VRRAM synapse should be designed to consider the initial value of the memristor to prepare the training conditions for high classification accuracy. These results mean that a synaptic circuit using 3D VRRAM will become a key technology for implementing neural computing hardware.

Self-Selective Resistive Device With Hybrid Switching Mode for Passive Crossbar Memory Application

In this letter, we experimentally demonstrated a novel resistive device with a hybrid switching mode that can be alternated between volatile threshold switching and non-volatile resistive switching. The device consists of dual-functional layers VO2 /HfO2 sandwiched by symmetrical TiN electrodes. A >20 unified ratio for selectivity and memory window is obtained. Owing to the stable resistive behavior of HfO2 and insulator-metal transition of VO2, the device shows excellent uniform switching parameters in both switching modes with a high on-state current density (1E4 A/cm2) and fast switching/recovery speed (< 30 ns). This self-selective resistive memory is of great potential in the high-density crossbar array, particularly for the future 3D-Vertical resistive random access memory (RRAM) integration.

A Novel Three-Dimensional High Density Vertical Rram Arrays with Reduced Leakage Current

NAND flash is the most widely used non-volatile memories due to its low cost and rewritable properties. However, some problems exist in NAND flash that need to be solved, such as low writing and reading speed, high operating voltage, and relatively large cell dimensions. Next generation memories, especially resistive random access memory (RRAM) could improve these problems. The technologies of 3D vertical architecture have made a major breakthrough in establishing high-density memory structures. Combined with an array structure, a 3D high density vertical resistive random access memory (VRRAM) cross-point arrays are able to efficiently increase the device density in vertical direction.In this work, a novel 3D VRRAM arrays which contains two VRRAM cells (Pt/ ZnO/ Al and Pt/ ZnO/ Ti) and one normal layered RRAM (Al/ Al2O3/ Ti) at each cross point were constructed. The Al2O3 based device could operate under a lower compliance current, where contained higher resistance than ZnO based cells. With the controlling of compliance current, the leakage current between VRRAM cells and Al/ Al2O3/ Ti cell could be effectively reduced, therefore, the neighboring cells could be operated independently. We demonstrated the introducing of VRRAM structure could achieve high density advantage in vertical direction, and the adjacent cells in 3D vertical structure could be operated independently with scarcely leakage current. The shrinkage of leakage current enhance the stability of devices and increase the feasibility of high density VRRAM structure applications. Figure 1

Demonstration of 3D Convolution Kernel Function Based on 8-Layer 3D Vertical Resistive Random Access Memory

3D Convolutional Neural Networks (CNNs) has been widely used for medical image analysis such as magnetic resonance imaging (MRI) and video recognition due to their intrinsic 3D characteristics. This letter presents 3D convolution operations realized by an 8-layers 3D vertical resistive random access memory (VRRAM) under a field-programmable gate array (FPGA)-controlled relay-matrix based test platform. As an implementation, 3D Prewitt operators are used for edge surface detection of 3D version MNIST handwritten digits with ${16}\times {16}\times {16}$ pixels. The experimental results show that 3D convolution kernels can be correctly implemented on our in-house 3D VRRAM with higher parallelism than the conventional architecture. Besides, the proposed 3D VRRAM meets the demands of low power and high capacity for 3D CNN accelerator, paving the way of 3D VRRAM-based processing-in-memory (PIM) architecture for 3D CNNs.

Three-Dimensional (3D) Vertical Resistive Random-Access Memory (VRRAM) Synapses for Neural Network Systems.

Memristor devices are generally suitable for incorporation in neuromorphic systems as synapses because they can be integrated into crossbar array circuits with high area efficiency. In the case of a two-dimensional (2D) crossbar array, however, the size of the array is proportional to the neural network’s depth and the number of its input and output nodes. This means that a 2D crossbar array is not suitable for a deep neural network. On the other hand, synapses that use a memristor with a 3D structure are suitable for implementing a neuromorphic chip for a multi-layered neural network. In this study, we propose a new optimization method for machine learning weight changes that considers the structural characteristics of a 3D vertical resistive random-access memory (VRRAM) structure for the first time. The newly proposed synapse operating principle of the 3D VRRAM structure can simplify the complexity of a neuron circuit. This study investigates the operating principle of 3D VRRAM synapses with comb-shaped word lines and demonstrates that the proposed 3D VRRAM structure will be a promising solution for a high-density neural network hardware system.

Thermal effect in ultra-high density 3D vertical and horizontal RRAM array

Thermal induced reliability issues in ultra-high density storage arrays are becoming increasingly significant, especially in 3D stacks. In this paper, research into the reliability issues of a stacked 3D TaOx vertical resistive random access memory (VRRAM) is demonstrated first through experiments. The results indicate that the thermal induced reliability issues of a 3D VRRAM are very severe. To further study the thermal effect and its impact on a 3D VRRAM, finite element analysis models of the 3D VRRAM are established in CMOSOL Multiphysics. Parameters including different feature sizes (F), metal layer thickness (tm), isolation layer thickness (ti) and the number of stacked layers (Ns) are simulated and investigated to clarify the relationship between heat dissipation performance and the device’s integration density. In addition to this, the influence of electrode materials is also discussed. The results reveal that scaling down F and tm are the main reasons for bad heat dissipation performance, and optimizing the thermal conductivity in both electrodes and the isolation layer can improve the heat dissipation performance. In addition, a comparative investigation into the heat dissipation performance in a 3D horizontal RRAM (HRRAM) has also been performed under the same conditions. Finally, by evaluating the influence of the integration parameters on the thermal effect related reliability performance, design guidelines for a 3D VRRAM and HRRAM are proposed.

Interface engineered HfO2-based 3D vertical ReRAM

We demonstrate a double-layer 3D vertical resistive random access memory (ReRAM) stack implementing a Pt/HfO2/TiN memory cell. The HfO2 switching layer is grown by atomic layer deposition on the sidewall of a SiO2/TiN/SiO2/TiN/SiO2 multilayer pillar. A steep vertical profile was achieved using CMOS-compatible TiN dry etching. We employ in situ TiN bottom interface engineering by ozone, which results in (a) significant forming voltage reduction which allows for forming-free operation in AC pulsed mode, and (b) non-linearity tuning of low resistance state by current compliance during Set operation. The vertical ReRAM shows excellent read and write disturb immunity between vertically stacked cells, retention over 104 s and excellent switching stability at 400 K. Endurance of 107 write cycles was achieved using 100 ns wide AC pulses while fast switching speed using pulses of only 10 ns width is also demonstrated. The active switching region was evaluated to be located closer to the bottom interface which allows for the observed high endurance.